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Abstract: Wind power systems are often installed where is remote,inaccessible or the climate is not suitable for human to stay for long time. So the traditional scheduled maintenance and breakdown maintenance can not meet the demands. It is very essential to ensure re- liable and stable operation,and to reduce the cost of maintenance,condition monitoring and fault diagnosis of wind power system. The existing fault diagnosis methods for the main failure components of wind power system,including gear boxes,generator,power electronic devices,blade and so on,were introduced and classified. This work will provide an effective reference to the improvemet of the reliability of wind power system,reduction of cost and promoting the engineering process.
According to the conditions of the low level of automation and low labour productivity,an automatic control system for flotation process was designed and developed innovatively based on the ControlLogix series software and hardware platform of Rockwell Company. The structure and function of the control system are introduced carefully. The control strategies of the pulp level,the air flowrate and the flotation reagent feeding are described in detail. The control software and human - machine interface are designed and developed. The actual industrial application results show that the control system operates steadily,safely and reliably,the worker labor intensities are decreased obviously,and the work condition is improved,the competitiveness of the enterprise is enhanced.
A analysis method of the problems of interconnected systems and the decentralized controller design method are presented in this paper. That the topology of the interconnection is arbitrary and the exchanged information between subsystems is lost cause the unreliable link. Firstly,the stochastic variable responsible for the situation of the lossy communication is regarded as a source of model uncertainty, and the system is modeled in linear fractional transformation. Then the robust control theory is employed for system analysis, and the largest probability of communication failure,which is tolerated by the interconnected systems keeping mean square stable,can be obtained. Decentralized state feedback controllers are designed to ensure that the whole systems are mean square stable for a given communication failure rate,based on the technique of linear matrix inequalities. Finally,an illustrative example of power network which is a part of China southern power grid is presented finally to show the effectiveness of the proposed model and method. Simulation results demonstrate that when the communication faults occur between the power components in the China southern power grid,using the method described in this paper can make the network well designed and wide-area controlled and the local power network mean square stable.
he position and pose accuracy of the robot end depends on the geometrical parameters accuracy of each joint. An easy and practical method of parameter identification method is put forward to improve the pose accuracy. Using the robot kinematics equation depending on D-H algorithm,the relationship between the end and each joint is obtained. This particular method as well as the corresponding experiment is designed to solve the problems in the identification process,the actual geometric structure parameters are obtained according to the accurate measurement by laser tracker. The experimental results verify the effectiveness of the presented method.
As a key parameter in blast furnace smelting process,the temperature of molten iron is of importance for smooth operation of blast furnace and the energy consumption. This paper studies on the important indicator for heat state of the blast furnace,namely molten iron temperature. By taking advantages of both method of K-means clustering and support vector machine( SVM) ,a K-means clustering - based SVM model is proposed for predicting the temperature of molten iron. Firstly,the training sample data are divided into m classes and m SVM regression prediction models are established accordingly. At the same time,a particle swarm optimization algorithm is utilized to optimize the model parameters. Then,m discriminant functions are established to recognize which class the sample data belongs to. Finally,the sample data are put into the corresponding class of regression model to predict temperature. Compared to the standard SVM - based prediction method,the proposed method predict the molten iron temperature with a higher accuracy.
On-ramp metering is an effective tool for traffic management on freeway networks. In this paper,based on the ordinary differential equation( ODE) model of the ramp system,a new fuzzy self-tuning PI controller for freeway ramp metering is designed. By considering the upstream and downstream traffic flow information sufficiently,the designed PI controller can effectively suppress the influence of the system disturbances. Furthermore,a fuzzy logic is esigned to tune of the PI controller parameters automatically. Compared with both PID and ALINEA approaches,the simulation results illustrate the validity and efficiency of the presented method intensively.
A building block approach is presented to design Glareshield of a simulator. On the basis of its function categorization,this approach aims at designing independent functional blocks through appropriately lowering relative dependence on connections between blocks of operating board and control board,realising separation of function and structure,module segmentation and data communication between modules. Furthermore,taking communication between blocks as an example,independent communication blocks were designed, which effectively prevented frequent occurrence of system crashes due to code disorder in communication. Through experiments in applications,it proves that the building block design approach can greatly improve the reliability of the simulator. Furthermore,this approach could also be applied in the design of front roof and center control console.
The dynamic pricing model of the perishable product with the price-dependent demand using the optimal control theory is established. The objective is to maximize the total sales profit within the sale planning horizon. The optimal conditions of the sale price are obtained according to the Pontryagin maximum principle. And if the sale price is between the unit purchasing cost of the product and the upper limit of the sale price,a lower limit of the optimal sale price and a higher limit of the optimal inventory level at every time point of the sale planning horizon are obtained which are both related to the perishable rate and the unit holding cost of the product at every time point of the sale planning horizon.
In accordance with the control features of material proportioning process of the ladle refining furnace,e. g. ,inertia,time lag,non-linearity,a kind of compound control algorithm is proposed based on particle swarm optimization algorithm( PSO) , error back propagation( BP) neural network and proportion integration differentiation( PID) algorithm. The PSO-BP-PID compound algorithm is applied in a 150t ladle refining furnace burden weighing control system. The particle swarm optimization algorithm with global optimization characteristics improves the convergence of the BP neural network which the initial weights of BP neural network is optimized. The optimized BP neural network is then used to adjust PID parameters on-line. The PID controller based on PSO and the BP neural network controls real-time the proportioning process of the ladle refining furnace. The simulation and operation experimental results show that the control effect of the PSO-BP-PID algorithm is better than the control effect of the traditional PID algorithm. The control system of the ladle furnace ingredients based on PSO-BP-PID algorithm can significantly improve the accuracy of ingredients,and effectively solve the contradiction between ingredients weighing speed and accuracy.
To the speciality of mixed noise constituted by pulse noise and Gauss noise,we present a comprehensive algorithm in this text,which is based on the simplified PCNN model,utilizing several technique specialities of the model,selecting parameters properly, and combining with mathematical morphology method,median filtering and wiener filtering. This method performs better than average filters and median filters on hybrid noise reduction while retaining edges and detail information of the image. Experiments show that the effect of eliminating grey image mixed noise which applying the simplified PCNN eliminating algorithm proposed in this paper is good. This algorithm can show a big advantage when in the comparison with other algorithms. This algorithm not only can effectively filter hybrid noise but also can excel in real-time tasks because of its reduced computation complexity. With the increase of the image populated by blends noise,the advantage is obvious.
By using the decoupled double synchronous coordinate system software phase locked loop ( DDSRF-SPLL) ,it can detect the unbalanced grid voltage of positive sequence component and negative sequence component,and put forward the coordinate transformation of the mathematical to establish the grid side of directly-driven permanent wind generator's mathematical model,which includes network side PWM inverter control and Voltage phase locked loop. A kind of current decoupling control strategy is proposed. It uses the space vector pulse width modulation ( SVPWM) on AC output current control. This method can independently control the output current of the active and reactive component,and can make the output current waveform of sine and grid voltage in phase with the same frequency. Thus it can greatly reduce the harmonic pollution. It can eliminate the influence of unbalance voltage,and to achieve a precise phase-locked output current and gird voltage three-phase grid voltage amplitude imbalance. In this paper,using Matlab /Simulink software platform,the simulation proves the validity and feasibility of the control system when the grid stays in balance and unbalance. This system improves reliability of wind power grid connection.
Using classical Lie symmetry theory,boundary control for a class of parabolic distributed parameter systems has been studied, and the control laws on open loop and closed loop have been designed to control the system reaching the fixed stationary state. By means of infinitesimal generators and invariance condition,the analytic control conditions on open loop and closed loop are presented after having the classical Lie symmetry of the system,which is expressed by infinitesimal generators. The control purpose can be satisfied under the two types of control laws by setting system parameters,initial condition and control purpose ahead. Comparatively,from the simulation,the convergence of output error under open loop control is slower than that under closed loop control,and however there are overshoot phenomena nearby the intake under closed loop control. The result proposed in the paper can be applied for controlling a class of temperature or concentration models with conduction and convention characteristics.
The uncertainty of the Power carrier communication network makes it unable to form a network,using the general communication networking method. To solve this problem,this paper designed a improved genetic algorithm applicable to path optimization. The algorithm can rapidly and accurately build global communication network. Firstly,the genetic algorithm is used to all communications nodes and communication paths for global searching. Then,when the communication node breaks down,the genetic algorithm will use its global optimization characteristics to build communication network. During the network process,Dijkstra algorithm and the thought of Graph Traversing algorithm are introduced to overcome the shortcomings of the genetic algorithm,such as,easiness to converge to a local optimal solution and small Processing scale. Finally,according to the most optimized reserved principle of The Niching Technique, the improved genetic algorithm can get the best network results. Matlab simulation experiments verifies the convergence and feasibility of the algorithm. The algorithm improves the efficiency and rapidity of network to meet instantaneous and accuracy requirements of the Power carrier communication dynamic network,and has practical value.
The proportional relation of the current of stator and rotor is indirectly used to analyze the relation of the rotor current and rotor position observer. By analyzing the simulation results a vector control strategy for doubly - fed induction generator ( DFIG) based on PQ - power rotor position observer is proposed. This control strategy overcomes the drawback of incorrect observation in other speed observer methods when running at the synchronous speed. The proposed control strategy has wider speed range,and can achieve the control performance similar as the control method using speed sensor. Finally,a DFIG experimental system is built up and the experiments are performed. The simulation and experimental results have a good agreement verifying the effectiveness of the proposed control method.
Time-delay is frequently encountered in many control problems. The existence of time-delay induces difficulty in stability analysis and may thereby lead to oscillations or degraded performance in closed-loop systems. This paper investigates the stability analysis of linear time-invariant( LTI) systems with time delay depending upon the solution of the characteristic equation. Not only the absolute stability but also the relative stabilities the transient performances are depended on the locations of the characteristic equation roots. The existence of time delay transforms the closed-loop characteristic equation into a transcendental equation with an infinite number of roots. By investigating the real part and the imaginary part of the transcendental equation,the characteristic equation can be transformed into a rotation equation with two vectors related to the system parameters. The characteristic roots on the given region boundaries can be determined exactly and concisely. Numerical examples are provided to illustrate the proposed algorithm.
In this paper,the technical limitations of minimizing resource losing model and maximizing threat model are analyzed via reviewing the current research of weapon target assignment ( WTA) problem. To solve the non - real - time problem with the existing methods,a dynamic WTA method is proposed which accords with the practical combat. Firstly,to make the allocation model being real - time and general,the dynamic input of target is introduced at random. Secondly,aiming at the dynamic random system with target inputs, the state of rebirth time is analyzed,and the system is transformed into a Markov process by embedded Markov Chain. Then the system state transition probability is resolved. Thirdly,the objective function is constructed,and the target assignment dynamic model is established by state transition matrix. Finally,the solution of model is given,and its validity and real - time characteristics are proved. The method proposed in this paper refines and complements current weapon target assignment models.
Robust fault tolerant control of networked control system ( NCS) with variable sampling period and time delays was studied in this paper. First,a discrete - time model with parameter uncertainties lying inside a polytypic framework whose vertices were determined through the Real Jordan form approximation is proposed. Based on the model,an observer was proposed to estimate the fault and guarantee the convergence speed of fault estimation. Furthermore,the robust active fault tolerant controller based on dynamic output feedback was designed to guarantee the stability of the closed - loop system. A sufficient condition for the existence of controller to guarantee the system asymptotically stable is established in terms of linear matrix inequalities ( LMIs) . When the LMIs have an optimal solution, explicit expression of the desired optimal H∞ tolerant fault controller can be determined. Finally,numerical simulation is presented to illustrate the effectiveness of the proposed design techniques.
The grain of strip steel can be turned to uniform. And the work hardening and inner stress can be eliminated by continuous annealing line. So it is the key for improving steel’s mechanical properties. But in a real production process,the mechanism of continuous annealing is complicated. The coupling operation parameters could change the characteristics of strip steel,and the detection of steel hardness has a long time lag,which brings grate obstacle to improve steel quality. PLS is used to build the relationship between average trajectory process and hardness. It can guarantee the safety of production process by realizing hardness prediction and process monitoring timely. Simulations in real process shows the feasibility and effectiveness of the proposed method.
Aimed to improve the seam tracking precision while welding,this paper proposes a laser vision seam tracking system which is composed of a laser vision sensor,a deviation correction controller,two stepping motors and a cross slider. While the tracking system runs,the laser vision sensor measures position deviation between the seam gun’s current position and desired position,and the controller determine the correcting value,and the motors drive the cross slider to modulate the seam gun’s position and posture so as to offset seam tracking error. The prototype of the system was constructed. Seam tracking experiments show that the tracking error can limit to being less than 0. 5mm. It is concluded the laser vision seam tracking system exists wide application prospect in precision welding engineering.
On the basis of the analysis and comparison of the existing power cable insulation monitoring methods,it designs the online monitoring system of the underground power cable insulation approach for the operational characteristics of the underground coalmine cable lines. The system uses the low-frequency signal injection method,injecting the low-frequency signal into the cable through the reactor neutral point. It constitutes a loop of the low - frequency signal,the cable insulation resistance and the Earth. Under the circumstances of injecting certain low-frequency voltage,it can achieve on - line monitoring of the cable insulation,by detecting the value of the low-frequency current-line,so the problems of poor reliability and low sensitivity on-line insulation monitoring of the power cable are solved better. The experiment shows that this method has a low error and high accuracy in detection; it can monitor not only the single cable insulation but also the multiple ones. It can apply to both the main cable circuits and the branch ones.
How to quickly control the main steam temperature to the setpoint is difficult problem in the field of fire power station control. Considering the control problem of the main steam temperature of super critical boiler machine set,a neuro-fuzzy model based adaptive PID cascade control for the main steam temperature of fire electrical engineering set is proposed in this paper. A neuro-fuzzy model for main steam temperature is established by using the information of attemperation water flow,inlet temperature of high temperature superheater,main steam flow and the main steam temperature. Then the adaptive PID cascade control of main steam temperature is implemented. A PI controller is employed in the inner loop to overcome inner disturbance,which can decrease the influence of the leading area's differential temperature. Then the single neuron PID controller ( SNAC) is implemented to control the called generalized process,which includes PI closed loop and inert segment. This controller is adjusted to eliminate the error between the predicted value and the output value. Finally,the effectiveness of this methodology is verified by simulation example.
With the background conditions,this paper gives an accurate model to describe the passenger’s evacuation in ships. Also an improved ant colony algorithm is given by using a new heuristic function and convergence conditions. The new algorithm can increase the influence of social factors and psychological factors in personnel evacuation. The simulation result shows that it can successfully solve the problems and it is better than basic ant colony algorithm in both time and efficiency. It can provide a new idea in this kind of problems.
Word sense disambiguation is one of the first problems in natural language processing system so far. It trys to solve the problem of word sense disambiguation in natural language processing by Sense Pruning using HowNet. We proposes that the objective of WSD is to reduce the number of plausible meanings of a word as much as possible through“Sense Pruning”. After Sense Pruning,it will associate a word with a list of plausible meanings. It would like to keep the truly correct sense of each word on its own meaning list. Developing a human-machine mutual word sense tagging system and two set of criteria were used for the evaluation of Sense Pruning algorithm: recall rate and reduction of the number of possible meanings of a sentence. Effects of the size of the analytical window and the analytical unit were studied.
Aiming at solving the problem of poor availability of the physical parameters of a practical overhead crane,this thesis proposes a parameter identification method based on energy reservation. It builds up regression equations for identification based on energy reservation principle,and then applies non-negative least squares algorithm to deal with data,obtaining the controller design model of overhead crane. This thesis has the identification experiments done on the overhead crane experimental system simulating the characteristics of the operation of practical overhead crane and gets the mathematical model of the system. On the basis of the mathematical model, an LQR controller has been designed to testify the accuracy of the identification results,confirming that the parameter identification method can help a lot on control of overhead crane.
To ensure virtual enterprise ( VE) reaches the desired goals,the owner should design effective mechanisms to avoid moral hazard. In view of the VE exists principal agent relationship between the owner and the partner,we incorporate the partner’s fairness preferences psychology and design an effective incentive mechanism considering the partner’s fairness concern to prevent the moral hazard problem,and compared to the traditional principal agent model. Analysis results show that the fairness preference has changed some of the conclusions of the traditional principal agent model that influences the effort level and the revenue sharing. The owner should try to choose the partners with lower intensity fairness preferences psychology to avoid moral hazard in order to increase its own profit.
The random time delay brings great challenges to the controller design for the Internet - based teleoperation system,the worst case is that it can destabilize the Internet - based teleoperation system. First it gives a brief historical preview of bilateral passive control theory and the wave variable control approach,and then a passive scattering transform method,extending the passive bilateral control method to the Internet - based random time delay teleoperation system,is advanced to guarantee the teleoperation system’s stability with any non - symmetric random network time delay. Finally,a virtual master - slave manipulator bilateral control scheme is designed for mobile robot teleoperation based on the passive scattering transformation method,and simulations are carried out to verify the results at the end of this paper.
Aiming at the control problem of glide stage to cruise stage,considering rotation of the Earth and stamping constraint and initial state constraint of the cruise stage,control law design model has been build. Optimal control problem have been translated to nonlinear programming problem Using Gauss pseudospectral methods. Nonlinear programming problem has been solved by SNOPT. The simulation results show that Gauss pseudospectral methods can solve optimal control law problems of glide stage to cruise stage.
Quadrotor Unmanned Helicopter ( QUH) is a kind of under - actuated system with multiple inputs and strong coupling. It has a broad application prospect,such as aerial photography,archaeology,patrolling border,anti - terrorism investigation. Dynamical model can be established according to Euler's theorem and Newton's law,considering the effect of air resistance and rotating torque. A dual - loop control system is designed based PID algorithm,and then another system is designed based on fuzzy self - tuning PID controller. Simulation results of two controllers reveal that the QUH can arrive at the specified position and keep hovering and control effects of fuzzy self - tuning PID controller are better than that of classic PID in the aspect of response time and stability.
The attitude control is the key of the automatic flight control for quadrotor UAV. In order to improve the robustness of quadrotor UAV for various uncertainties,a robust adaptive backstepping controller is designed. The whole attitude kinematical model is obtained and translated into a MIMO nonlinear system with generalized uncertainty. Because the design feature of the system is strict feedback, the backstepping controller is designed. A robust adaptive function is designed to counteract the influence of the uncertainties, which include external disturbance and interior parameters perturbation. The nonlinear tracking differentiator is introduced to estimate the differential signal of the virtual control law and to reduce the“computer explosion”problem which is ubiquitous in the backtepping controller. The closed - loop system is proved to be stable and converge exponentially through constructing Lyapunov function. Simulation results are presented to corroborate the effectiveness and robustness of the proposed control strategy.
Considering the severe changes of aero - dynamic parameters and susceptibility on the external disturbances of nearspace vehicle ( NSV) ,a backstepping control strategy with robust adaptive dynamic surface is proposed in this paper for the disturbance problem in the NSV longitudinal trajectory system during the hypersonic process. Firstly,the complex nonlinear longitudinal dynamic model is transformed into nonlinear non - affine model by using the input - output feedback linearization method. Then,the“computer explosion“in the computation procedures for derivatives is avoided with the estimation of the virtual control law in one - order low pass filter. The robustness item in the virtual controller combined with approximation capability of neural network is used to eliminate the parameter uncertainties and external disturbances in NSV. The simulation results show the improved robustness in this method with reduced complexity.
Since the disadvantages and limitation of anti-collision system with radar in aircraft ( ACSR-anti-collision system with radar in aircraft) ,the terrain avoidance strategy was proposed,which adopted the virtual radar to substitute the airborne radar system to solve the problem of the terrain anti-collision. According to characteristics of aircraft motion,uniform motion model,uniformly accelerated motion model and the current statistic model were established,which are suitable for fast and accurate prediction. The principle of the interacting multiple model and Kalman filtering were adopted to predict the aircraft flight conditions in real time which were used to match the airborne terrain database to avoid crash. The prediction arithmetic was testified by simulation,and the results show that the prediction time has important effect on deviation. But the warning level can be determined according to the forecast time. The research demonstrated that terrain avoidance is much more advanced than ACSR,and it can replace ACSR.
In view of the nonlinear characteristics of the SPMSM drive system,the unified PCHD modeling and speed control of PMSM is presented based on the interconnection and damping assignment of energy-shaping method and port-controlled Hamiltonian systems with dissipation( PCHD) theory,to obtain high performance of the speed control system. First,from an energy-balancing point of view, uncertain system unified PCHD modeling is established including the inverter,SPMSM with iron losses and mechanical load. And then, the passive controller of the SPMSM drive system is designed,and the nonlinear disturbance caused by the inverter is compensated using the extended state observer. Finally,speed regulator is designed by using ADRC in order to get the desired q axis current,both the design and the implementation of the controller becomes simpler and easier. The simulation results show that the proposed method can ensure global stability,strong robustness. The system achieves a satisfactory dynamic and static performance.
BP algorithm usually has slow convergence speed and is easy to fall into local minimum value. On the basis of the analysis and study for the domestic market of air conditioning order. This article proposes a method of the model parameters of grey neural network optimized by genetic algorithm. By utilizing the property of gray model that the randomness of data can be reduced and the strong nonlinearity of neural network,the method establishes a non-linear prediction model for air conditioning demand,and the genetic algorithm is used to optimize it so as to improve the accuracy of forecasting and speed up the degree of convergence. Simulation results indicate that the algorithm can better solve the problem of air conditioner order forecasting and can be widely apply to similar prediction.
The classical newsvendor model ( CNM) is often used to study the supply chain coordinating problem with perishable products in single period,and only one order is permitted in the CNM,which is inappropriate in fact. However,for some perishable products with the special consumed time,such as the moon - cake for Middle - Autumn Festival,etc. ,the manufacture for these items provide a price discount for advanced booking in order to reduce inventory level. This paper considered a two - stage supply chain composed of a risk - neutral supplier and multiple competing loss - averse retailers,and investigated the combined impact of the competition and retailer’s loss aversion attitude on the decision - making behavior of retailer and the coordination of supply chain within the advanced - booking discount contract. Based on game theory,it shows that in this supply chain game,there exists a unique symmetric pure Nash equilibrium,and the optimal total order quantity increases as the degree of competition increases but decreases as the loss aversion increases. Moreover it is found that,advance - booking discount contract can coordinate the supply chain. At last,the effectiveness of advanced - booking discount Contract is verified in supply chain by a case study.
The traditional mutation strategy of differential evolution algorithm can not reach a good balance between the global search and the local search and the operators are constant. The differential evolution algorithm leads to premature convergence and the low search efficiency. Based on analysis of the performance of the optimization strategies,a hybrid mutation strategy is proposed in this paper. The scheme attempts to balance the exploration and exploitation abilities. In this way,emphasis is laid on the global search at the beginning,which results in maintaining the diversity of population. Later,contribution from the local search increases in order to converge to the optimal faster. Meanwhile,the random normal scaling factor F and the time - varying crossover probability factor CR are used synchronously to improve the performance of DE. Finally,the modified differential evolution algorithm is tested on benchmark functions. The simulation results show that the modified algorithm can effectively avoid the premature convergence,as well as modified the global convergence ability and the search efficiency remarkably.
For a class of uncertain systems with only output available and nonlinearities in the presence of unmeasured state,a state observer based on input signal replacement is firstly established by using the neural networks,then,an output feedback adaptive control scheme is proposed by employing dynamic surface theory. The controller parameters' range is initially selected by theoretical analysis. It is guaranteed that all signals in the closed - loop system are semi - global uniformly ultimately bounded. Parameters optimization strategy is proposed by using genetic algorithm. Then the optimal initial input and controller parameters are chosen,so the problem of input impulsion is solved and tracking precision is improved.
The voltage level to reactive power optimization dispatch and control problem is incorporated. A model of reactive power optimization is established based on multi-objective differential evolution,which takes into account of loss minimization,voltage level best target. Considering the drawbacks of traditional differential evolution ( DE) algorithm such as premature and slow search speed,a strategy of self-adapting parameter improved differential evolution algorithm was proposed and first applied in reactive power optimization problem. By adjusting the mutation F and crossover CR during the evolution process,the diversity of population is increased and the global search area is expanded,which avoids algorithm into a local optimal solution,at the same time,the convergence speed is accelerated later. The simulations are carried out on IEEE-14 bus system,and the results show the validity of the proposed algorithm.
On the basis of SVM( Support Vector Machine) multiclass classification,an improved DAGSVM ( Directed Acyclic Graph Support Vector Machine) hand gesture recognition approach is put forward. Firstly,depth information of the scene is collected by using Kinect sensor and hand region is obtained. Then feature vectors are extracted,which are used to train multiple binary SVM classifiers. DAGSVM classifier is constructed using DAG topological structure with trained binary SVM classifiers and its structure sequence is improved. Finally,the experimental results proved that the improved DAGSVM could reach higher recognition rate and can be used in the control of intelligent wheelchair successfully.
The state feedback H∞ control problem for networked control systems with quantization and random packet dropouts is discussed. The random packet dropouts from the sensor to the controller and from the controller to the actuator are considered. The quantizer considered here is dynamic. The state feedback H∞ controller can be constructed via solving a linear matrix inequality. A quantized H∞ control strategy is derived,with a condition on the quantization range and the error bound satisfied,such that the closed - loop system with quantization and random delays exponentially mean - square stable and with a prescribed H∞ performance bound. An example is presented to illustrate the effectiveness of the proposed method.
In order to meet the needs of the system dynamic range and response time,this paper proposed a FPGA - based rapid IF automatic gain control ( AGC) system. First,computer simulation is carryed out using Quartus II simulation to verify the timing and logic functions of the system algorithm. And then the design and implementation of the system hardware are completed. Finally,the paper realizes a control module inside the FPGA,up to 60 dB of dynamic range control. The validation of the logic simulation software and testing of the hardware circuit proved that the response of the system is fast and has high control accuracy,and systems have achieved better results in the actual radar receiver,and the proposed method increases the dynamic range of the receiver to improve the performance of the radar system.
The construction and techno - realization of high - speed data acquisition system are presented. The FPGA is adopted to realize the design of the hardware,which includes analog signals filtering plastic circuit,high - speed AD interface circuit,USB interface circuit. The system application software design is also analyzed in detailed,which includes FPGA program design,USB firmware program, upper computer application software,etc. The experimental results show the feasibility of the designed method.
This paper extended Bernstein and Hollot's robust stability test to linear time-invariant ( LTI) uncertain sampled-data control systems. Unlike that of the most previous works,it directly uses the data of the continuous-time plant and therefore it is expected to be less conservative. This paper uses exponential-like structure construct an upper bound on the standard quadratic Lyapunov function. This upper bound is independent of the uncertain parameters but a free scalar parameter which is artificially introduced to reduce the conservativeness of the upper bound. This paper gives out a reliable numerical algorithm. And then this algorithm is improved so that it can be used to check the robust stability condition for system with an arbitrary GSHF.
The formation control for underactuated surface ships with 3 degree - of - freedom is investigated,and the shape tracking controllers based on Leader /Follower method are developed. The ship is controlled respective,when the system received the shape-command. The Leader tracked the shape-command directly,and the Followers tracked the designed trajectory,which is designed by the shape-command and the state of the Leader. For the design of tracking control,firstly,reference tracking path and reference forward speed are designed based on desired formation shape. Secondly,path tracking controller and speed tracking controller are constructed by nonlinear control theory and Lyapunov theory. Finally,the shape tracking error of the follower is uniformly ultimately bounded. Numerical simulations show the system can tracking any curves. The results validate the effectiveness of proposed controller.
Resource conflict between the project caused by the limited resources is an important factor affecting the construction period and benefits of the construction programme. The building of an accurate model which can describe the information about resource management, such as relations among tasks,resource requirements and task duration and so on is the key to the process management and optimal allocation of resources for implementation stage of construction programme. Resource model for implementation stage of construction programme was defined based on HTCPN by analyzing the resource characteristics of non - depletable resources and the modeling requirements for implementation stage of construction programme. The model can accurately describe the whole process of programme, and also can find resource conflict,analyze the construction period and choose the optimal resource planning by simulation. With CPN Tools simulation platform,taking an example of a construction programme,the authors built its resource model and verified the correctness and effectiveness of the model.
The operation control for complex industrial processes consists of two layer – loop control layer and operational control layer. The former aims at achieving the required loop control for each production unit along production line whilst the later optimizes the set-points to the control loops so that certain performance indexes (such as product quality and energy cost) is optimized when the closed loop controlled variables follow their set-points well. This paper presents a novel method that can be used to analyze the performance deterioration of the optimized operational control, where the impact of tracking errors of loop control to the optimized performance is quantitatively formulated when the tracking errors are small. It has been shown that loop tracking errors would generally deteriorate the optimized performance in a quadratic way – leading to the establishment of the square impact principle (SIP). Moreover, it has been shown that the production infrastructure will also affect the deterioration of the optimized performance. Formulation on the analysis on the flat robustness (FR) and randomness of the optimized performance indexes will also be made and several issues on the future studies are listed.
For the multivariable control system, interaction analysis and variable pairing are the first step for the control system design. In order to handle the problem that RGA do not reflect the dynamic characteristic among loops, all kinds of improved dynamic relative gain array are presented. The paper presents a new variable pairing method based on multivariable state feedback model predictive control. It can fully reflect the dynamic and stead-state information about control process. Through the optimization of the prediction horizon, the correlation index array can be identified. Combining the correlation index array and steady state array, the final pairing array is defined. Several cases and the comparisons with the existing methods indicate that the proposed method is a useful tool to give the best pairing scheme.
To improve the static and transient stability of disturbances with all scales in power system, this paper provides a control strategy of an online self-turning stabilizer based on the parameters adjusted by a fuzzy neural network. Compared with the conventional fuzzy stabilizers, the strategy adds a fuzzy neural network parameter adjuster which stores the fuzzy rules in the neural network combing a qualitative description and a quantitative numerical calculation. The adjuster makes full use of the neural-network’s associative memory and parallel processing ability, adjusting the conventional fuzzy stabilizers’ parameters of quantitative factors and scale factors on line quickly and dynamically. According to the experiment, compared with IEEE PSS2B and conventional fuzzy stabilizers, the proposed control strategy can increase the damping of the power system effectively, strengthen the ability to withstand different scales of disturbances, with strong adaptability and robustness, and improve the static and transient stability of the power system greatly. The proportional control strategy has a broad prospect in industrial application.
Abstract: An embedded multifunctional data acquisition system was designed for intelligent autonomous mobile robot to detect its operating environment and status information. The system contains four acquisition circuits for ultrasonic sensors, six signal collection circuits that support the collection for both voltage signal from 0V to 5V and current signal from 4mA to 20mA, eight digital signal output channels, eight digital signal input channels, and the data acquisition system interface data with other device via PC/104 bus. This paper introduced the composition of the data acquisition system, detailed designed the bus interface circuit,ultrasonic detection circuit, the digital signal input-output circuit, and the analog signal detection circuit for the system. With the characteristics of simple structure and good practicability, the system has a good application in the detecting of the operating environment and status information of intelligent autonomous mobile robot.
A Parameter adaptive iterative learning control scheme for trajectory tracking in a finite duration is proposed for a class of discrete nonlinear system with unknown time-varying parameters and linearly growing condition. It discusses the convergence performance of the algorithm and improves convergence rate using forgetting factor. By using least squares algorithm with a forgetting factor in discrete adaptive control, a new parameter adaptive and controlling law is constructed in iterative field. Pointwise convergence in time domain and gradual convergence in iterative domain are proved with Lyapunov-like function. Two simulation examples are provided to validate the efficacy of the proposed parameter adaptive law and controller.
For the problems that Hough transform can not correct the license plate without frame border and Radon transform is computation-intensive and time-consuming, a fast algorithm combined Hough transform and Radon transform for license plate tilt correction was proposed. By setting effective detection areas and making full use of Krisch to get the horizontal edge, this method can correct both the rim license plates and the rimless ones fast and effectively. Selected 200 artificial slanted pictures to verify the accuracy of angle inclination got from the algorithm, and selected 400 natural slanted pictures to compare the speed and accuracy of the three algorithms, namely Hough, Radon and the method in this paper. Performed in Matlab platform with the angle ranged in [-20°,20°], the average execution time of the proposed algorithm is 0.38s and its accuracy achieves more than 90%. Experimental results show the algorithm is fast, accurate and robust.
Based on the mathematical model of direct-driven wind turbine with permanent synchronous generator(D-PMSG) the sliding mode observer(SMO) is built to achieve estimating rotor position and speed and its improvement. Saturation function to replace the discontinuous switching function to weaken the SMO inherent chattering defects. In addition, for the traditional method of selection of low-pass filter to extract continuous signal caused by the phase delay problem, this paper constructs the EMF observer to replace the low-pass filter is directly extracted from the back EMF signal, eliminated the phase compensation and improved the estimation accuracy. Eventually established a vector control system of direct-driven PMSG wind turbine based on improved SMO. By comparing the simulation analysis,the results that the system can accurately estimate the rotor position and speed, and has a good dynamic and static performance. And further experiments to verify the feasibility and effectiveness of the program, with the value of engineering applications.
The same space voltage vector is chosen by Bang-Bang control in the traditional Direct Torque Control (DTC) system in the case of both big and small error. To solve the problem, it is proposed that the hysteresis and voltage vector table are replaced by a fuzzy controller, so as to increased torque dynamic response speed and reduce the flux and torque ripple. Fuzzy logic is introduced. The errors on the torque and the stator flux linkage are fuzzy classified and the space voltage vector can be reasonable obtained according to the errors .Through fuzzy logic calculate, suitable space voltage vector is obtained to control induction motor. A design method of the fuzzy controller is presented particularly. Hardware and software design of the fuzzy direct torque control system is given. The results of simulation indicated that the response of the system is quicker when the motor start or the rotate speed breaks , torque and flux ripple is smaller, the dynamic and static performance is improved. There are good results in the occasions that require fast dynamic torque response.
Consider a constrained LPV system with polytopic description. An offline design of state observer is proposed when the system state cannot be observed. And the new self-tuning system constructed by state observer error is proved to be robust stable, which guarantees the output of the observer can converge to the true sate value. After that, the parameter dependent Lyapunov function (PDLF) is introduced to obtain poly-quadratically stable control laws by solving the min-max optimization problem with infinite horizon performance cost. And the output feedback control law can guarantee the feasibility and stability of the computational paradigms. Compared with the traditional strategy, the offline design method of observer can greatly reduce the online computation and the utilizing of PDLF can obtain the less conservative condition for the LPV systems. And at last, the simulation results show the effectiveness of the proposed method.
In order to satisfy the high speed and high accuracy request of the X-ray angle sorter, high-speed data acquisition system based on the DSP has been developed which includes X-ray diffraction signal recuperation module, high-speed data sampling and process module and high-speed USB communication module. Signal recuperation module amplifying and filtering the X-ray diffraction signal, the DSP board carrying out high-speed data acquisition and processing for the analog signal, acquisition the signal of photoelectric encode, and transmitting the collected data to the host computer through USB interface. The system application software design is also analyzed in detailed. Furthermore, in order to get the high precision, The separated the FIR algorithm has been embedded to the DSP procedure for filtering data. The experimental results show the feasibility of the designed method.
It presents a feedforward control strategy based on linear time-invariant disturbance observer for nonlinear controlled objects. At first, choose a linear time-invariant model as target model which have a desired dynamics. And then describe the non-linear properties of the controlled object as an equivalent input disturbance variable of the target model, which can be extended as state variables, so that the target model with the disturbance becomes the substituting model of the controlled object. Thirdly, design an observer of the substitute model to get the estimated value of the disturbance variables. Finally, a feedforward control can counteract the influence of the disturbance on the controlled object. The design theories of this method are confined within the architecture of linear system theory, and independent of the mathematical model of the controlled objects. Particularly, the controller is without any nonlinear part. In circumstances of reversible controlled object, the existence theorem of the substituting model is proved. At last, for the identical discrete time controlled object, comparative simulation research is performed between this control method and a nonlinear internal model control based on support vector machine ?th-order inverse system method. The simulation results indicate that the method proposed in this paper is better in linearization, anti-disturbance capability and system robustness.
Affected by the internal exothermic reaction and the entrance temperature, etc, the polypropylene production process has the characteristics of time-delay and strong inertial, based on discrete auto disturbance rejection control, this paper use tracking differentiator and extended state observer to arrange the transition process and estimate system disturbance rationally, then design a non-linear error feedback control law by the error signal, realize the temperature control of polypropylene reaction axe. At last with the MPCE experiment device and SIMATIC PCS 7 controller, temperature control experiment under disturbance or variable load is studied, the results show system with ADRC ensures very good robustness and adaptability under different conditions, and has better dynamic performance than classic PID in the same operation conditions.
To improve the self-navigation ability of mobile robots, an optimal robot orientation estimation method in urban areas is proposed. This method is based on the vertical vanishing point in one view and the horizontal vanishing point correspondences across two views. To obtain an optimal estimation result, an error-aware vanishing point estimation method is developed, leading to a minimum variance solution. By analyzing the error propagation in computing vanishing point, we convert the minimum variance estimation problem to a convex optimization problem, which is well studied in operations research. An orthogonality check is proposed for each candidate horizontal vanishing point. Then, a vanishing point matching method is developed to find the correspondences. Some physical experiments are carried out to validate the accuracy and time-efficiency of our method.
Aiming at the rapid trajectory tracking control with the modeling error and uncertain disturbance problem of the joint robot, this paper presents a design of the improved neural network-PID controller. The architecture employs dual-controller mode. Through studying the PID controller’s input and output characteristics the neural network can compensate the modeling errors and uncertain disturbance of the joint robot rapidly, and using the LSM and the input and output characteristics of the converged neural network optimize the control parameters of PID controller can weaken the effect of modeling errors on the trajectory tracking control. The controller is stable in the Lyapunov sense. In the paper, we have a simulation experiment taking the two linked manipulator as the plant. Finally, the superiority of this design is demonstrated through the experimental results.
To solve the problem that nonlinear dynamic inverse method is relatively dependent on the accurate system model and easily affected by inverse error, a nonlinear robust adaptive controller design using Squares Support Regression (SVR) is proposed in this paper,in which SVR is used to identify inverse model of the controlled system and construct adaptive compensation term to eliminate the inverse error caused by uncertain factors, such as inverse model error or disturbances, and so on. This could make the system outputs track the outputs of the reference model rapidly and accurately. The updating rule of SVR parameters is derived, and the stability of the nonlinear system is proved by Lyapunov stability theory. Furthermore, the simulation results of a typical nonlinear model show the feasibility and robustness on solving a class of nonlinear problems of the proposed method.
Capacity allocation strategy of a single facility with random demand and private information, in which the earning per unit of each organization as well as the manufacturing capability of facility are private and the demand of each organization is both private and random, is investigated in this paper. According to the information distribution, the whole problem is decomposed into the facility problem and organization problems whose corresponding stochastic programming models are also given. A negotiation-based method (between the facility and organizations) is proposed to solve the capacity allocation problem. First, the proposed stochastic programming models are equivalently clarified to be parametric programming models, and then parameters design of the negotiation mechanism is performed by Lagrangian relaxation and Taylor series expansion. Further, a distributed deflected sub-gradient method is derived to update Lagrangian multipliers, and in the end the solution algorithm to the capacity allocation problem of a single facility with random demand and private information is given. Finally, numerical examples are employed to analyze the effect of some important parameters on capacity allocation results and demonstrate the efficiency of the proposed method.
The dynamic system can be more accurately described by fractional-order differential equation due to the ‘unlimited memory’ principle of fractional-order calculus. Unlike the analysis and simulation of integral-order system which are frequently completed in the SIMULINK environment, the same procession for fractional-order systems are mainly realized in MATLAB workspace at present as the absence of corresponding SIMULINK blocks. In this paper, a block was designed which can display fractional-order transfer function, thus fractional-order system can be analyzed in the SIMULINK environment based on fractional-order calculus theory and SIMULINK mask technology. This block is used as convenient and intuitionistic as the integral-order one. By engineering application, it is validated that the designed block is more useful for the design and analysis of fractional-order system.
As the important part of high-speed the EMU traction drive systems, the traction motors provide the most direct impetus to the EMU. Synchronous machine has small volume, light weight, high power factor characteristic, to adapt to the needs of the running of the actual high-speed train is the new generation high speed train traction motor. This paper introduce a direct torque control strategy, the synchronous motor in traction, constant speed and braking mode control technology, and in the train network control system framework, based on the Rt - lab system for the real-time simulation research. The results show that the method possesses a torque response is rapid and the accuracy is high, has the practical application potential.
Non-minimum phase systems (NMPS) existed widely in engineering field such as electrical power engineering and space engineering. Because there are zero, pole points or delay links of the system transfer function in the right of complex plane, the accuracy of traditional control method is poor. Grey extra-deleting control theory is applied to design the controller of NMPSs. An expected system which has good performance is designed and compared with the original system to find out the excess, and then extra-deleting feedback is applied. The feedback can change the system transfer function and offset or weaken the excess which deteriorate system performance. The grey extra-deleting controllers are designed for some NMPSs and are studied compared with classical PID controllers. Simulation results indicate that the proposed controller can improve the performance of the system significantly and can solve the problem that non-minimum phase system is difficult to control.
The harmonic current detection method based on adaptive noise cancellation principle has been researched extensively and proved to be feasible. Invariable step-size Least Mean Square adaptive algorithm can't solve the contradiction between convergence speed and steady-state error, this paper presents a variable step-size adaptive harmonic detection algorithm with momentum term, which uses the momentum term to speed up the convergence of weights and uses the autocorrelation estimates of the current and previous error signal to adjust step-size iteration. This algorithm has significant enhancement of harmonic detection in real time and accuracy, and good engineering application prospects because of the small calculation quantity and easy realization. The simulation results verify the effectiveness of the algorithm.
Most power system dynamic state estimation algorithms under hybrid measurements assume that the information can be available at the same time and rarely consider the impact of bad data on state estimation. Aiming at the problems above and taking full advantage of hybrid measurements, a sequential robust dynamic state estimation is proposed based on the analysis of the SCADA estimation time window of the mixed-measure system. Firstly, the algorithm fusions the phasor measurement unit(PMU) measurements sampling at the same time and then the supervisory control and data acquisition(SCADA) measurements are taken into estimation in sequential of arrival time. Then the cost function is calculated. A set of SCADA/PMU measurements is formed and used for state estimation via extended Kalman filter(EKF) if the cost function exceeds a threshold The algorithm can make good use of utilization of the estimation center, reduce the impact of measurement outliers on the state estimation and improve state estimation real-time and accuracy. Finally, simulations on an IEEE 14-bus test system demonstrate the effectiveness and superiority of the proposed algorithm.
This study is dedicated to the design of an intelligent controller of motorcycle which consists of digital igniter, remote data transceiver module, Passive Keyless Entry module and so on. It can help to increase the engine power, economy and improve the pollution emissions and fuel efficiency, and make the motorcycle achieve a better performance of anti-theft. In order to reduce the oil consumption and exhaust nitrogen oxide efficiently, this paper introduces the accurate matching approach about best ignition time and best ignition pre-angle with speed of engine, applies the digital igniter to control the ignition pre-angle accurately, and balances the engine’s power and oil consumption. We utilize the Passive Keyless Entry module to verify the ID of motorcycle driver, locate the motorcycle by GPS remotely, get the current Chinese address through a search algorithm. It will be more flexible for users to supervise their motorcycle by acquiring the motorcycle’s localization in multi-ways. The experiment results indicate that this controller can locate the motorcycle precisely, the real-time monitoring system is robust, and the anti-theft device is available.
With the expansion of interconnected power grid, the influence of random factors on power system stability is becoming increasing prominent, and the stochastic stability of power system has become an important research subject. On the basis of the analyses of the existing research work, the purpose of this paper is to discuss weak stochastic asymptotic stability of original stochastic system by using of the stability of its truncation system. Firstly the weak stochastic asymptotic stability theorem is proved in probability. And taking the power fluctuation as random excitation, nonlinear stochastic differential equations model of one single machine infinite bus (OMIB) system is constructed. Then the weak stochastic asymptotic stability of OMIB system under small random excitation is verified by the Lyapunov function cited from reference [2]. Finally the paper gave corresponding conclusions.
In order to solve the congestion phenomenon in Internet, a sliding mode active queue management(AQM) algorithm is proposed. Considering the effect of uncertainty and time-varying delay factors in Internet, an asymptotically stable sliding surface is designed by linear matrix inequality(LMI) based on the Internet congestion control model. It can effectively overcome the effect of these time-varying factors. The controller is designed by the way of reaching law, which can effectively constrain the oscillation of the queue length in router. Simulation contrasts demonstrate that the proposed algorithm possesses better stability and robustness, and it can adapt to the time-varying Internet environment. An application effect of the proposed sliding mode AQM algorithm in a large electric power enterprise remote monitoring and control network is given.
Ball Mill Load Condition Recognition Model Based on Regularized Stochastic Configuration Networks
Multi-car Elevator Systems Using Dynamic Zoning Based on Fast R-CNN
Blade Tip Clearance and Blade Tip Timing Measurement Based on Microwave Sensors
Multi Core SVM Fault Diagnosis of Diesel Engine Based on Dimension Measurement
Design and Simulation of Fractional Order Controller for Linear Inverted Pendulum
Design of the Double Fuzzy Controller System for AA0 Sewage Treatment
Background Modeling Based Payload Swing Angle Measuring Method of Bridge Crane System
Modified Teaching-learning-based Optimization Algorithm for No-wait Flow-shop Green Scheduling Problem
In this paper, a modified teaching-learning-based optimization algorithm, namely MTLBO, is proposed to minimize the energic power cost criterion of the no-wait flow-shop green scheduling problem with sequence-dependent setup times and release dates, which considers a serial of environmental impacts and the rising energy costs in recent years. Firstly, a speed-up evaluation method is developed according to the property of the algorithm. Secondly, in the teacher phase, the overall quality of the population can be improved by Insert operation for the learner with the worst grades or the problem solution. Meanwhile, a self-adapting teaching factor is put forward to improve the global search ability of MTLBO. Thirdly, the insert-neighborhood local search is proposed to strengthen the local search capability, which contributes to achieving a reasonable balance between global and local search of the algorithm. Simulation results and comparisons show that MTLBO is more robust and efficient than the other optimization methods.
Application of BDD Algorithm in Overhead Contact Line System Failure Risk Assessment
Voltage Stability Strategy of Self-excited Induction Generator Based on Predictive Current Control
Improved EDA Solving Green Reentrant Job Shop Scheduling Problem
Control of Revolving Inverted Pendulum Based on PSO-FOPID Controller
Design of a Rapid Arctangent-based Tracking Differentiator
In order to solve the problem that the arctangent tracking differentiator has difficulty in setting parameters and slow convergence near the equilibrium, the author has designed a fast convergence arctangent tracking differentiator. A rapid arctangent tracking differentiator is constructed by introducing linear functions and terminal attractor functions into the arctangent function and its stability is proved, which makes the system far from the equilibrium and close to the equilibrium point convergence rapidly and stably to the equilibrium point. This continuous function form of linear and nonlinear combination enhances the tracking ability of the system, and effectively suppresses the noise, which can realize fast and precise signal differential and tracking, and the form is simple and easy to implement. The simulation results show that the rapid arctangent tracking differentiator has excellent performance.
Bearing Fault Diagnosis Based on EEMD-Hilbert and Optimized Cyclic Spectrum
Robust Attitude Control of Hypersonic Vehicle Based on Active Disturbance Rejection Control
The Two-layer Classifier Model and its Application to Personal Credit Assessment
Research on Tracking Control Strategy of Maximum Efficiency for Wireless Power Transmission System
In the double closed loop speed control system of brushless DC motor(BLDCM), it was difficult to satisfy the application in high speed, high precision or large load disturbance using the PID parameters obtained with traditional optimization methods. Based on the research of standard particle swarm optimization (PSO), an improved PSO algorithm using orthogonal test mechanism was designed to optimize the PID speed controller, which solved the problem of slow optimization rate and easy to fall into local optimum in optimizing PID parameters with traditional PSO algorithm. By comparing the simulation results, it was indicated that the PID control method based on improved PSO algorithm had faster adjusting speed, smaller overshoot and strong anti-interference ability in the double closed loop speed control system of BLDCM. This method provides a new idea for the optimization of speed control system of BLDCM.
Path optimization algorithm of Multi-mode Automatic Guided Vehicle Based on MOWCA
Short-term Wind Power Multi-step Forecasting Based on Interval Type-2 Fuzzy Logic Systems Method
With the advent of the information age, satellite communications play an increasingly important role. As a typical representative of this field, SOTM (Satellite communication on-the-move) system has been widely used. Based on the analysis and study of servo control subsystem of the vehicle SOTM, a stepper motor servo control system is designed. The mathematic model of the actuator in the system is established by using the system identification toolbox in MATLAB. The servo system is simulated in MATLAB/Simulink. The speed-position dual closed-loop PID with disturbance compensation control algorithm is designed. The experimental results show that the proposed stepping motor servo control system can significantly suppress the disturbance of the carrier and meets the performance requirements.
Sliding Mode Variable Structure Control for a Class of Underactuated Systems
Richardson-Lucy Algorithm Based Defocused Bubble Images Restoring
Stabilization for Time-delay Rectangular Descriptor Systems
Distribution Network Reconfiguration Based on Improved Genetic Algorithm Combined Second-order Cone Programing
Design of Robust Controller for Rolling Based on State Space Model
Research on Viscosity Compensation Method Based on Improved Binary-tree Model
Aeroengine Adaptive PID Control Based on Hybrid Artificial Bee Colony Algorithm
Research on Low Speed Control Strategy of Hybrid Excitation Synchronous Machines
Control of Molecular Weight Distribution Based on Finite Order Moments
A Method with Adaptive Seed Point Substitution for Counting Overlapped Cell
The Vehicle Interior Sound Quality Prediction and Analysis Based on RBF Neural Network
Adaptive Robust Control of Chinese Medicine Sugar Precipitation
Multi-model Soft Sensor Modeling Based on the DP-RFR Method
A Two-person Interaction Recognition Algorithm Based on Active Curve Model
Parameter Estimating of Asynchronous Motor Based on Variable Frequency Excitation Response Test
Ventilation System Design of Urban Utility Tunnel Based on Fuzzy PID Control Algorithm
Detection of Construction Vehicles Under the Transmission Corridor in UAV Inspection
Method of Dynamic Positioning State Estimation Based on MCMC Particle Filter
Arc Length Control of Welding Machine Based on Variable Universe Fuzzy PID
Multi-objective Particle Swarm Optimization with Black Hole Mechanism and Chaotic Search
Status and Prospect of Earth Pressure Balance Control Modeling for Shield’s
Sealed Cabin
Mechanical Spindle Vibration Prediction Model Based on RBF Network
Stabilizing Incremental Model Predictive Control and Its Applications in Contouring Control
Performance Evaluation of Bulk Cargo Port Based on GHS Functional Neural Network
Soft Sensor for Intensive Aquaculture Process Based on GA-SVR
The Improved Finite Control Set Model Predictive Control Method to the New Inverter
Harmonic Frequency Spectra Spread of PMSM Based on Random Space Vector Pulse Width Modulation
Fractional Sliding Mode Control of Mine Motor Based on Load Observation
Research on Data Driven Modeling of Superheater Temperature Deviation based on Partial Mutual Information
Aiming at the requirement of maintaining constant tension in filament winding and the great tension fluctuation in the traditional tension control, the mathematic model is established on the basis of analyzing the tension controller of the pendulum pole. The fuzzy control combined with variable integral PID is taken as the control mode and its feasibility is analyzed. The experiment results show that the control method combining fuzzy control and variable integral PID is applied to the tension control system, the performance is better than the traditional PID control, and the change of the parameter has higher adaptability. It can solve the problems such as large fluctuation of tension in filament winding, low speed of yarn breaking and yarn return.
Positive Gait Recognition Method Based on Kinect Depth Data in Occlusion Scene
Aiming at the difficulty of recognizing the obstruction of the human body in public, a method of using Kinect depth data to solve the positive gait recognition in the occluded scene is proposed. First, the image is captured by installing a depth camera at the top of the entrance and exit of the surveillance area, and the image is segmented by the background subtraction method, the RGB color space is normalized to detect and remove shadows to complete the image preprocessing. Then, the periodic changes of the skeleton structure of the lower body area evaluated by the Kinect are extracted from the front view, and the feature sets corresponding to the rear view are extracted from the depth information of the shadow outline. These feature sets retain high-resolution gait action information. Finally, the unidentified frame of a cluttered test sequence is compared with the matched frame of the training sequence to complete the final recognition. Experiments show that this method is computationally efficient and achieves satisfactory results at different levels of occlusion.
Planning of Electric Vehicle Charging Station Based on Culture Algorithm
Moving Object Detection Methods Based on Adaptive ViBe
3 - DOF Helicopter Control Based on Variable Universe Fuzzy PID
Research on Energy Management for Ultracapacitor/Lithium Battery Hybrid Electric Vehicles
Design of a Banknote Thickness Sensor Based on Eddy Current Principle
Attitude Stabilization Backstepping Sliding Model Control in Non-cooperative Target Capturing Process
Adaptive Control for Hypersonic Gliding Vehicles with Unknown Parameters
Research on Obstacle Avoidance Algorithm for Mobile Robot Based on Hybrid Strategy
Regularized Nonnegative Matrix Factorization based on L21 Norm
Non-negative matrix factorization (NMF) adds non-negative constraints to matrix decomposition, and its decomposed sub-matrices are easier to interpret. The optimization target of many traditional NMF algorithms is based on L2 norm, and they are not easy to identify the nonlinearly distributed data structures. In order to solve this problem, we propose a regularized non-negative matrix factorization algorithm based on L21 norm. The objective function of the proposed algorithm is written into the form of L21 norm, and we add graph regularization term to the objective function to handle complex nonlinear data sets. Finally, several benchmark data sets are used to test the performance of the proposed algorithm on the clustering task. The experimental results show that the proposed algorithm can extract the key features of the data, obtain the low-dimensional representation of the original data, and produce better clustering results.
A Method of Batch-to-batch Adaptive Optimization Based on T-PLS
In this paper, an adaptive batch optimization method based on T-PLS is proposed to apply to the quality control and optimization of batch process. Due to the complexity of batch process, the model can’t perfectly match with the plant and result to NCO mismatch, in which the optimization result is just suboptimal. The gradient correction method is used to compensate the differences between the model and the plant. At the
same time, in order to expand the effective range of control variables, T-PLS statistics (,,) are
introduced as a hard constraint. Finally, the simulation is carried out to verify the effectiveness of the proposed method.
Study on the Control System of the Automatic Abrasive Blasting Robot for the Large Diameter Bend Pipe
Sliding Mode Control for 3D Path-following of Underactuated AUV
A Combined Particle Filter for Multiple Extended Target Tacking
Route Planning Based on Programmed Cell Death Evolutionary Algorithm
The mechanism of programmed cell death is simulated in evolutionary algorithm. Three artificial control genes are introduced for the optimization of the traditional genetic algorithm. The advanced achievement of biology is absorbed in this new evolutionary algorithm. It can effectively overcome the premature problem of genetic algorithm. Besides, it can also obtain both the optimal solution and several sub optimal solutions. The the feasibility and effect of the algorithm is tested in the vehicle route planning.
Guaranteed Cost Preview and Repetitive Control for Uncertain Linear Discrete Systems
Nonlinear Proportional Back-stepping Control for a Class of Pure Feedback Systems
Research on Anomaly Detection Algorithm Based on Regular Change Background
Hybrid Supply System Energy Management Strategy of Hybrid Electric Vehicle Based on Adaptive Filter
In order to improve the energy efficiency of the power supply system of hybrid electric vehicle (HEV), an energy management strategy based on adaptive filter is proposed. Firstly, the hybrid power supply system and the bi-directional DC/DC converter of HEV are modeled. Secondly, the adaptive law based on power-split is selected and the novel hybrid power adaptive energy management strategy is designed. Finally, the effectiveness of the proposed method is verified by the simulation experiment under different working conditions. The experimental results show that, the proposed method can transmit power of hybrid power system of HEV effectively to achieve high energy utilization.
Control Strategy of Torque Distribution for Optimizing the Comprehensive Performance of Distributed Drive Electric Vehicle
Faults Diagnosis of Three-level Inverter Based on EMD-DTRVM
Application of RBF-ARX Model in the Ship Course-keeping System
Based on the nonlinear characteristic of the ship, it is difficult to obtain accurate physical parameters of the ship physical model. Therefore, the statistical modeling method and RBF-ARX model is used to model the ship course-keeping control process. In this paper, the RBF-ARX model, the ARX model and the long-term predictive output of the physical model are compared, and the effectiveness and superiority of the RBF-ARX model in the ship course-keeping control system modeling are verified.
Design of LPV Fault Tolerant Controller for Wind Turbine Based on Combined Zero-process
The State Estimation and Fault Detection Algorithm Based on Projected Zonotopes
Event-triggered Control for Consensus of Linear Uncertain Systems
The Safety Analysis of Carrier-based Aircraft Landing Based on Baseline Statistical Methods
The Selection Method of Network Public Opinion Prediction Based on Fuzzy Information Aggregation
For the network public opinion prediction model selection problems in which the attribute values are in the form of hesitant fuzzy information and the input arguments are associated with each other, a novel hesitant fuzzy Heronian geometric mean (HFHGM) operator is proposed on the basis of Archimedean norm and Heronian geometric mean, whose properties are studied in detail. Then, some special cases of the HFHGM operator are discussed and the hesitant fuzzy weighted Heronian geometric mean (HFWHGM) operator is introduced. In addition, a new hesitant fuzzy multi-attribute decision making method based on HFWHGM operator is developed, which can capture the interrelationships among the input arguments and enable decision maker to select different parameters to make decision in accordance with their own risk preferences attitude. Finally, a numerical example about the network public opinion prediction model selection is provided to illustrate the effectiveness of the developed method.
Micro Electric Net Petri Model Based on the Structure of I Type Synchronous Decomposition
In order to solve the problem of poor synchronization, discrete and continuous events coexist, a micro grid Petri monitoring model based on the structure of I type synchronous decomposition is proposed to improve the accuracy of the model construction of micro grid. Firstly, the research for Petri net system model of micro grid system was conducted, and the Petri network based main network model, micro source energy, storage system model, the standby power supply model and load model were built; Secondly, in order to improve the performance of Petri network model, the network structure decomposition method was used to construct Petri model based on the structure of I type synchronous decomposition, Petri complex control was multiple sets of simplified local subnet control problems; Finally, the effectiveness of the proposed model is verified based on micro grid Stateflow monitoring simulation system.
Multi-objective Layout Optimization of Oil-gas Pipeline Network Based on NSGA- = 2 \* ROMAN \* MERGEFORMAT II
The Stability of Coupled Four Rotor Unmanned Aerial Vehicle based on ADRC Controller
Estimation of the State of Charge for Lithium Battery Based on D’STA - RBF Neural Network Algorithm
An Active Learning Algorithm Based on Imbalanced Datasets
Research on Learning Algorithm of Neural Networks Based on Improved Fading Unscented Kalman Filter
A Circular Target Localization Method from Degraded Images
Time Delay Aanlysis for Supercavitating Vehicles
Determination Method for Depth of CDBN Based on Reconstruction Error
Research on the Cloud Computing Models Based on the Improved Interval Cross Efficiency
Direct Torque Control of Brushless DC Motor Based on the Second-order Sliding Mode Observer
To solve the difficulty of acquiring back electromotive force (back-EMF) and the chattering problem existing in the conventional sliding mode control, a direct torque control (DTC) based on the second-order sliding mode observer (SOSMO) is proposed for brushless DC motor (BLDCM). According to the mathematical model of BLDCM, traditional linear sliding mode surface and its first-order derivative are selected to constitute the second-order sliding mode surface. Simultaneously, the control law was deduced by using an improved reaching law. Then, the SOSMO was designed, and it could make the sliding mode chattering be focused on the higher order differential of phase error and integrate the higher order differential term. So, the weaker chattering and faster convergence was guaranteed. Moreover, the back-EMF could be accurately estimated by SOSMO without an additional low-pass filter. The SOSMO was applied to DTC of sensorless BLDCM, and the torque ripple and system performance could be improved effectively. The simulation results show that the proposed method is superior.
A Design Method for Deep Belief Network Based on Reinforcement Learning
Research on Fast Defogging Algorithm Based on Depth Evaluation of B Channel Compensation
Sparse Auto Encoder Model Based on Firefly Learning Optimization and its Application in Bearing Fault Recognition
Research on Dynamic Access Selection of RSU in VANET
Prediction of Room Cooling Load Based on Improved ARX Model
Key Frame Abstraction and Retrieval of Videos Based on Deep Learning
Node Deployment Algorithm Based on Linear Programming in UWSNs
An EDCA Flow Balance Scheme Using Probabilistic Access in WMN
Dead-time Control of CNN DC Motor Based on Lyapunov Closed-loop Stability
Frequent Itemset Mining Using Prefix Tree in Big Data Environment
A Capacity Increasing Method of Using SAA for Wireless Mesh Network
Hierarchical Model Predictive Control Strategy Based on Model Switching Considering Economic Benefits
Control and Optimization of Semi-active Seat Suspension Based on Inerter
Dual-layer vibration isolation has good vibration attenuation effect, but its application in vehicle aggravates the vehicle load additionally. To solve this problem, this paper replaces the intermediate mass of dual-layer vibration isolation with Inerter, and further applies it into vehicle seat suspension. This paper first proposed a new seat suspension structure including magneto-rheological damper and Inerter, and derived the seat-human body motion differential equations. Based on these equations this paper presented a model-based (based on seat suspension dynamics model) control scheme, established the corresponding Simulink model, and optimized the parameters of both the seat structure and its controller with the Free-Search (FS) algorithm. This paper further completed the simulation tests using a compound excitation of three frequencies where the driver is easy to resonate,as well as a random excitation. These tests show that the proposed seat structure and controller parameters have better vibration attenuation effect.
Study on the Calculation Method of ω Input Signal in PSS4B Model
GNB Classification and Detection of Data Streams Based on Weighted Mechanism Concept Drift
Adaptive Control of Microgrid Power Balance Based on Network State Estimation
In the general stochastic nonlinear and non-Gaussian systems, the sensor faults including biased and scaled readings caused by sudden calibration errors have adverse effect on the precise monitor and stable control of the system. To deal with this problem, a novel diagnosis and identification method is proposed. An adaptive particle filter is developed to calculate the difference between the measurements and the particle filter estimates, then the type and magnitude of sensor faults are determined through with maximum likelihood estimation, thus the fast and precise detection of sensor fault is realized, and the adverse effect caused by the faults can be compensated. Some simulations are carried out on a boiler model, and the results validate the effectiveness of the proposed method.