Table of Content

20 February 2019, Volume 26 Issue 2

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Application of Adaptive Generalized Predictive Control Based on PSO in Microturbines

MA Cao-yuan, ZHU Xin-shang, HAN Yong-gang, GAO Ai-jie

2019, 26(2):  179-184. 

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According to the characteristics of large time delay, nonlinearity and time-varying in micro gas turbines, based on the theory of system identification and adaptive generalized predictive control algorithm, a new method of micro turbine speed control is presented. Frist, through the acquisition of the micro gas turbine rotor system input (fuel) - output (rotor speed) data, the CARIMA model of the micro gas turbine rotor speed system is identified by using the FFRLS algorithm. Then, an adaptive generalized predictive controller of PSO is designed based on the identified CARIMA model. Finally, simulations are carried out in MATLAB. Simulation results show that, when the load is abrupt, the fuel quantity is fast, the speed of the rotating speed is small, the tracking effect is perfect, the robustness is strong, and the control performance is also good.

Design of the Double Fuzzy Controller System for AA0 Sewage Treatment

HUANG Chao, BO Cui-mei, GUO Wei, TENG Gang

2019, 26(2):  185-190. 

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 According to the special requirement of aeration rate control in the aerobic pool based on the AAO sewage treatment technology, the intelligent control system is designed using the double fuzzy control algorithm. Through analyzing the principle of the control on the aeration rate of AAO technology, the double fuzzy controller is constructed based on the setting of fuzzy rule base, parameters are changed to advance the performance. Finally, this article describes the principle and the methods to accomplish the program of the double fuzzy controller by the use of programmable logic controller. This control method has been applied to the modern sewage disposal process, meeting the requirements of process and control.

Voltage Stability Strategy of Self-excited Induction Generator Based on Predictive Current Control

SUN Jing, YANG Da-liang, GONG Ping-ping, LI Zhuo

2019, 26(2):  191-195. 

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To deal with the voltage sag problem of the stand-alone self-excited induction generator (SEIG) when loading, a voltage regulation system based on the static synchronous compensator (STATCOM) is designed to maintain a constant terminal voltage of SEIG under loading conditions. In order to improve the transient performance and adaptation ability of the voltage regulation system, a predictive current control (PCC) method is proposed to design inner loop current controllers so that the transient performance and adaptation ability of the voltage regulation system will ultimately improve. Simulation results show that the proposed approach can improve the loading capacity and current tracking capabilities of SEIG very well compared with the classical methods. Terminal voltage performs well in robustness and regulation when loading. It has been shown that the proposed control method works very effectively.

Control of Revolving Inverted Pendulum Based on PSO-FOPID Controller

WEI Li-xin, WANG Hao, MU Xiao-wei

2019, 26(2):  196-201. 

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The rotary inverted pendulum is a strong coupling, nonlinear and unstable system which has one input and two outputs. In order to realize the effective control of the system in the stable stage, fractional order PID, PI^γD^μ, and FOPID are used in the pendulum angle and rotation angle's position and speed closed-loop control, respectively. Because of the large number of FOPID controllers and parameters, manual setting of the controller parameters is very complicated and not easy to achieve. Therefore, this paper uses the improved multi-objective particle swarm algorithm (IMOPSO) to adjust FOPID controller parameters. The effectiveness of the IMOPSO-FOPID algorithm is verified by the QUBE-Servo the rotary inverted pendulum. The inverted pendulum obtains good dynamic quality and stability, better than PID.

Research on Tracking Control Strategy of Maximum Efficiency for Wireless Power Transmission System

ZHAO Qiang, CUI Chang

2019, 26(2):  202-207. 

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The high frequency AC power supply for wireless power transmission systems is provided by the class D power amplifier, and the receiving end uses the DC-DC converter to optimize load. Base on this research, the problem of reducing the transmission efficiency caused by the dynamic change of load condition, coupling coefficient and frequency offset is solved using the maximum power tracking control strategy, which includes frequency tracking and load tracking. The efficiency of the wireless power supply system is analyzed by the equivalent model of the transmitter and receiver circuit, and the optimal value of the equivalent load impedance matching is obtained. An integrated control strategy based on frequency tracking by phase locked loop and load tracking by searching for the minimum input power operating point for a given output power is proposed to keep the system output power and efficiency constant. The experimental results show that the proposed control strategy can automatically track the frequency and load changes, so that the system can always keep the resonant state, track and maintain the maximum energy transfer efficiency according to the load condition.

Short-term Wind Power Multi-step Forecasting Based on Interval Type-2 Fuzzy Logic Systems Method

LI Jun, WANG Xing-hui

2019, 26(2):  215-222. 

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Aiming at short-term wind power forecasting, a method composed of interval type-2 fuzzy logic systems (FLS) with non-singleton type-2 fuzzification is proposed. Taking into account the stochastic nature of the wind power, a forecasting model using an interval type-2 FLS with non-singleton type-2 fuzzification is firstly built, the back-propagation algorithm is then used to update the parameters including the input membership function, the antecedent and consequent membership function respectively, finally, the SVD-QR algorithm is applied to the results of the BP algorithm to determine the reduced set of fuzzy rules, the training process iterates until the forecast accuracy can meet the design requirement or reach the specified training epoch. The employed method is then applied to real-world wind power forecasting instances, under the same conditions, compared to the existing forecasting methods including support vector machine(SVM), type-1 FLS, interval type-2 FLS with singleton fuzzification, interval type-2 FLS with non-singleton type-1 fuzzification, etc. Experiment results confirm that the employed method can achieve better forecasting accuracy while the fuzzy rules are reduced.

Distribution Network Reconfiguration Based on Improved Genetic Algorithm Combined Second-order Cone Programing

RONG De-sheng, DUAN Zhi-tian, HU Ju-shuang, LIU Jian-chen, ZHANG Lei

2019, 26(2):  223-228. 

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An improved genetic algorithm combined with second-order cone programing is proposed for distribution network reconfiguration to minimize power losses and eliminate voltage violations. The chromosome length is shortened by using the decimal genetic coding rule based on the loop. For eliminating the infeasible solutions generated during the optimization, the infeasible solution judgment method based on the switch loop-node matrix and the node layer strategy is adopted in this paper. Through the elite reserve and dynamic control mutation rate, the problem about the premature convergence of the genetic algorithm is effectively solved. Then, some equivalent conversion and relaxation are presented to cast the initial nonlinear power flow equation into a second-order cone model, which can reliably and efficiently solve the global optimality by using the available commercial software. Finally, case studies on IEEE 33-node test feeder are conducted and the results demonstrate the validity as well as effectiveness of the proposed method.

Aeroengine Adaptive PID Control Based on Hybrid Artificial Bee Colony Algorithm

CHEN Yu-han, XIAO Ling-fei, LU Bin-bin

2019, 26(2):  229-235. 

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 In view of the fact that the traditional artificial bee colony (ABC) algorithm has a slow convergence speed and traps into local optima easily, a new hybrid artificial bee colony (HABC) algorithm is proposed, which improves the optimization performance in three aspects: selection mechanism, neighborhood search mechanism and diversity of solution vectors. Aimed at the aeroengine control system, an online adaptive PID controller of optimum is designed based on this algorithm. In the control process, the parameters of PID controller are optimized constantly by HABC algorithm so that the aeroengine controller can adaptively obtain the time-varying optimal parameters according to the current system working status. Simulation results show that the aeroengine online adaptive PID controller implements the time-varying optimum of PID controller parameters, which insures that the closed loop system has good dynamic performance as well as strong robustness.

A Method with Adaptive Seed Point Substitution for Counting Overlapped Cell

CHEN Ming, YANG Hui-zhong

2019, 26(2):  236-240. 

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Cells of microscopic cell images are often adhesive. In order to count, it is usually necessary to find the seed points of the adhesion cells accurately. This paper proposes an algorithm of counting the overlapped cells based on pixel block scanning with good adaptability. On the basis of determining the area of the maximum and minimum in connected sections, the length of the maximum and minimum rectangular pixel block with same width side is determined. And then, the two value image is scanned by the maximum pixel block with width of side automatic decreasing from left to right, from top to bottom. The area matched to a near-circular pixel block is replaced with a seed point to get a seed point image and counting. A lot of experiments show that the method proposed in this paper has a significant effect on counting overlapped cells with different areas and has good adaptability.

A Two-person Interaction Recognition Algorithm Based on Active Curve Model

WANG Pei-yao, CAO Jiang-tao, JI Xiao-fei

2019, 26(2):  241-245. 

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In order to solve the issues in the two-person interaction recognition algorithm, such as the high dimension of the feature and weak representation ability, a novel algorithm based on the active curve model is proposed in this paper. Due to the advantage of the active curve to the sparse representation ability of the targets, the proposed algorithm obtains the deformable templates of the active curve by utilizing Gabor filter and Sum-Max Maps, then gives the sparse representation of two-person interaction. In addition, the proposed algorithm uses HOG features to describe all frames in the video sequences, then innovatively uses the distance of the extreme value method to get key frames of video sequences. Finally, the performance of the proposed algorithm is tested on the UT-Interaction dataset. The experimental results show that the active curve model extracted in the key frames is simple and has better representation ability, which obtains better interaction recognition rate. So the sparse representation of the algorithm in the field of interaction recognition has good research prospect.

Detection of Construction Vehicles Under the Transmission Corridor in UAV Inspection

WU Jin-ting, ZHAO Xiao-guang, YUAN De-cai

2019, 26(2):  246-250. 

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In order to solve the problem of construction vehicles under the transmission corridor in UAV inspection, an automatic detection algorithm for construction vehicles based on image processing and machine learning is proposed. First of all, the image collected by the UAV is preprocessed; secondly, according to the color and linear structure features of construction vehicles, two extraction methods of construction vehicles are given, effectively narrowing the range of recognition; finally, this paper chooses the method based on HOG feature and support vector machine (SVM), and gives a construction vehicle identification method. The experimental results show that the proposed algorithm can detect the presence of construction vehicles in complex scenes under the transmission corridor, and has good accuracy.

Multi-objective Particle Swarm Optimization with Black Hole Mechanism and Chaotic Search

XIA Yu, WU Peng

2019, 26(2):  251-257. 

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MOPSO is easy to fall into local optimum in the late stage of the algorithm, in order to prevent the "premature", the multi objective particle swarm optimization algorithm with black hole mechanism and chaotic search is proposed. The random black hole mechanism is adopted to search around the lead particle. By characteristics of chaos ergodicity, the search area around individual optimal point has expanded, so as to increase the diversity of the population and prevent falling into local optimum. The solution of improved ZDT series functions show that the algorithm can solve the problem of high dimensional solutions in 2-dimensional space, and the solution of the improved DTLZ series functions show that the algorithm can effectively solve the 3-dimensional space.

Stabilizing Incremental Model Predictive Control and Its Applications in Contouring Control

ZHANG Quan-peng, HE De-feng, WU Sai-nan, YU Shi-ming

2019, 26(2):  258-263. 

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This paper presents a stabilizing incremental model predictive control (MPC) algorithm for discrete-time multivariable linear systems with measurable disturbances. Taking the incremental state-space model as the predictive model, the finite horizon optimal control problem of MPC is formulated and the corresponding MPC controller is determined, which has a structure combining measurable disturbance feedforward with time-delay state feedback. Using the Lyapunov-Krasovskii stability theory of time-delay systems, we establish some sufficient conditions guaranteeing the stability of the closed-loop system with no constraints. Finally, the simulation example of a constrained biaxial contouring control system is employed to illustrate the validity of the algorithm proposed here.

Harmonic Frequency Spectra Spread of PMSM Based on Random Space Vector Pulse Width Modulation

GAO Ying, WANG Jia-jun, SUN Jia-hao

2019, 26(2):  264-269. 

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In the voltage source inverter fed permanent magnet synchronous motor (PMSM) driving system, there exists a large amount of voltage and current harmonic components concentrating around the switching frequency and its integer multiples for the traditional space vector pulse width modulation (SVPWM). In order to reduce the voltage and current harmonics of the inverter, the random carrier SVPWM is applied to spread the frequency spectra. This method can change the distribution of the harmonic components of the VSI and alleviate the acoustic noise, undesirable vibration and electromagnetic interference, which improves the control performance of the motor. Single random carrier space vector pulse width modulation (SRCSVPWM) and chaotic SVPWM are introduced. Simulation results in Matlab/Simulink show the effectiveness of the control method. In order to further prove the effectiveness of the random SVPWM on the frequency spectra spread, this paper evaluates the method by using the harmonic expansion factor (HSF) and the total harmonic distortion (THD).

Design of Tension Control System for Filament Winding

2019, 26(2):  270-275. 

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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.

Attitude Stabilization Backstepping Sliding Model Control in Non-cooperative Target Capturing Process

YIN Chun-wu, LIU Su-bing

2019, 26(2):  276-281. 

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A robust attitude controller has been designed for the rigid body spacecraft to satisfy the demand of rapid attitude stability with bound external disturbance and rotational inertia perturbance. The changing characters of the rotational inertia in the process of spacecraft capturing non-cooperative target is analyzed. A state feedback controller is designed for the kinetics equation, to ensure the attitude angle stabilize quickly. Then, a sliding mode controller is designed for the dynamics equation, to offset the strong external torque disturbance and system parameter perturbance. Finally, theoretical analysis certifies that the closed-loop system is global asymptotic stable under the robust controller. Under the strong uncertain simulation environment, in which, each sample point will produce a bounded random perturbance, simulations verify the controller performance of speedability, strong robustness and low energy.

Sliding Mode Control for 3D Path-following of Underactuated AUV

WANG Xiao-wei, YAO Xu-liang, BU Su-wen, WANG Feng

2019, 26(2):  282-288. 

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To deal with the problem of 3D path-following control for underactuated autonomous underwater vehicles (AUV). Virtual guidance law is introduced based on Serret-Frenet frame, the 3D path-following kinematic error model is established on the basis of considering the angles of attack and sideslip. First of all, the kinematics controller is designed according to the theory of Lyapunov by introducing two approaching angles. Then the dynamic controller is developed based on the theory of sliding mode control (SMC), so that the asymptotic stability and robustness of the control system can be guaranteed. In order to reduce chattering, the uncertainty of the model is estimated through the disturbance observer. Simulation results show that the controller has good robustness to the model parameter uncertainty. Accurate tracking of the 3D path can be realized.

Nonlinear Proportional Back-stepping Control for a Class of Pure Feedback Systems

Chen Long-sheng

2019, 26(2):  288-294. 

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A novel control approach based on the combination of back-stepping design and nonlinear proportional control is presented for a class of uncertain pure feedback systems. Based on the double performance functions, the nonlinear proportional controller is developed. Meanwhile, during every step of back-stepping, a Nussbaum function and a nonlinear proportional controller are adopted to construct the nonlinear proportional back-stepping controller to satisfy the requirements of unknown parameters, unknown structure, unknown control direction, unknown initial states and prescribed performance. The design procedure and structure of the proposed scheme are very simple with least adjustable parameters. Finally, the stability of the close-loop system is proved based on Lyapunov stability theorem. The simulation results demonstrate the feasibility and validity of the proposed control schemes.

Explicit Model Predictive Control of Quad-Rotor Aircraft

YANG Fan, DIAN Song-yi, WANG Lun

2019, 26(2):  295-301. 

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A binary search algorithm combining the truncated binary tree algorithm and distance search algorithm is proposed to deal with the complex problem of explicit model predictive control (EMPC) in the online search phase. On the basis of the fast binary tree algorithm, the pretreatment time is further reduced by the truncation condition, and then the partition search is performed on the leaf nodes by using the distance search algorithm. Finally, the method is applied to the online phase of the explicit model predictive control of the quad-rotor aircraft. The feasibility of the method is verified by the numerical simulation and the hardware-in-the-loop experiments.

Event-triggered Control for Consensus of Linear Uncertain Systems

KE Ya-wei, PU Nan-nan

2019, 26(2):  302-307. 

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In this paper, a new kind of distributed event-triggered control strategy is proposed for the consensus of general linear uncertain multi-agent systems. By considering the state uncertainty, input uncertainty and external disturbance in the control model, the event-based control algorithm and event-triggered condition are designed. Using Lyapunov stability theory, it is proved that the bounded consensus can be achieved for all agents. Finally, a numerical example is given to verify the theoretical result. The specific structure is as follows: for a class of systems with state uncertainty and input uncertainty, a distributed event-driven control algorithm is designed. Furthermore, for each agent, the algebraic Riccati equation is used to derive the distributed event-driven conditions. It is proven that the asymptotical consensus can be achieved by Lyapunov stability theory. Finally, a class of aircraft systems are taken as an example to demonstrate the effectiveness of the proposed method.

Multi-objective Layout Optimization of Oil-gas Pipeline Network Based on NSGA- = 2 \* ROMAN \* MERGEFORMAT II

LIU Qiang, MAO Li

2019, 26(2):  308-313. 

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This paper presents a multi-objective layout optimization method for the oil-gas pipeline network based on NSGA- = 2 \* ROMAN \* MERGEFORMAT II. Pipeline construction costs and gas collecting station construction costs are formulated as major objectives, and the model of multi-objective optimization in oil and gas pipeline network layout planning is established. By using non-dominated sorting genetic algorithm- = 2 \* ROMAN \* MERGEFORMAT II (NSGA- = 2 \* ROMAN \* MERGEFORMAT II) to solve the multi-objective problem, polygon is used to describe the obstacles, and obstacle avoidance is handled using the visual graph method. The proposed method considers multi-objective optimization of the oil-gas pipeline network and obstacle avoidance constraints, so it has good general theory and practical application value. Finally, a numerical example of oil and gas pipeline network is provided to verify the feasibility of the proposed method.

An Active Learning Algorithm Based on Imbalanced Datasets

ZHAO Xiao-qiang, LIU Meng-yi

2019, 26(2):  314-319. 

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Aiming at the problems of noise data and low classification accuracy in the classification process of imbalanced datasets, an active learning SVM classification algorithm based on improved SMOTE is proposed. This algorithm uses the attribution values of the minority class samples for training the sample set to choose and control the number of synthetic minority class samples by the majority vote method. According to the distance formula, the hyperplane is determined. The same number of majority class samples which are closest to the classification hyperplane are selected to form a balanced sample dataset. Support vector machine (SVM) is used to classify and obtain an optimal classifier. Then active learning is used to the imbalanced dataset which removes the training samples to circulate classification until samples of the imbalanced dataset is null by using the optimal classifier. Using UCI data, the experimental results show that the proposed algorithm can effectively reduce noise influence for data classification and improve the classification accuracy of the imbalanced dataset.

Determination Method for Depth of CDBN Based on Reconstruction Error

WANG Gong-ming, LI Wen-jing, QIAO Jun-fei, SHEN Zhao-xu

2019, 26(2):  320-326. 

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In order to determine the number of hidden layers in continuous deep belief network (CDBN), a determination method for the depth of CDBN based on the reconstruction error is proposed. CDBN is composed of multiple continuous restricted Boltzmann machines (CRBM). By analyzing the relevance between the reconstruction error and the network energy, and setting the threshold of the reconstruction error, the decision mechanism for the depth is designed to realize self-organizing adjustment for the depth of CDBN. The experiments show that the determination method for the depth of CDBN based on the reconstruction error can determine the optimal depth of CDBN and improve the efficiency of decision depth for CDBN.

Research on the Detection Algorithm of the Vehicle and Vehicle Distance Based on the Single and Double Camera Switch

DU Yan-qi, CHEN Meng-yuan

2019, 26(2):  327-335. 

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The range of interest (ROI) of the vehicle detection is defined after the lane has been detected, then according to the characteristics of the vehicle vertical direction, shadow under vehicles and taillight features to lock the front vehicle position, and the feature information of vehicles are converted into vehicle distance information finally. The algorithm proposed in this paper treats differently with day and night, and utilizes the strategy of single and double camera handover to detect the vehicle distance, which makes the system in short distance vehicle distance detecting with excellent performance. Experiments show that, this system designed in the paper, achieves the anticipated function, can accurately detect multi-lane and short distance vehicle distance, and the efficiency of the system also meets the real-time requirement.

Functional Safety Analysis of Level Transition Process of CTCS-3 System

2019, 26(2):  336-342. 

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Aimed at the complex compositeness of level transition scenes of the CTCS-3 system, according to the CTCS-3 system functional requirements specification, selecting the demand of the system life cycle stage, the safety of level transition scene is studied. Firstly, based on the UML extensibility mechanism, the hybrid UML model is established, complex compositeness of level transition scene is described. Secondly, according to the CTCS-3 system functional requirements specification, functional requirements are summarised, the correctness of level transition scenes is verified. Then, by analyzing the level transition process, the possible faults of function modules of system components are found out. The fault model of level transition scenes is established using FFDN. By integrating the PHAVer model and the fault model, the PHAVer model containing faults is built. Finally, functional module failures when functional requirements of level transition scenes are not satisfied are analysed with PHAVer, the function safety analysis of level transition scenes is implemented.

Research on Power Grid Fault Forecast Based on Abductive Reasoning Network

LIU Xiao-qin, WANG Da-zhi

2019, 26(2):  343-348. 

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Fault diagnosis is usually judged according to the information after fault happens. In order to prevent the fault before the occurrence of the fault, model prediction (MP) and abductive reasoning network (ARN) are proposed to predict the power grid fault. MP predicts the trouble-free operation of the data of the power grid using the historical data, compares with the actual grid runtime data and calculates the difference. ARN handles complicated relationships between data processing and the corresponding candidate fault section using a hierarchical network with several layers of function nodes of simple low-order polynomials. The combination of model prediction and abductive reasoning network can locate the fault before the protection device and circuit breaker acts, and has the function of fault early warning. The simulation results show that the diagnosis system can obtain rapid and accurate diagnosis results compared with the neural network method.

Design and Analysis of Infants Sleep-monitoring System

SHEN Xiao-bin, GU Tian-hua, CAI Bin-Bin, YE Hong-ji, ZHAO Jin-hui

2019, 26(2):  349-355. 

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In order to realize the monitoring and recognizing of the infant’s sleep quality, a sleep monitoring system based on a simple chip microcomputer is proposed, which correspondingly includes a hardware design and a software system. The hardware system is mainly constructed by a MK60FX512VLQ15 system board, pressure sensors and a LCD display. The key working process of the software system is to use the data collected from the pressure sensors placed on the crib, and then calculate the parameters values of location, fluctuation range and change frequency of location by the proposed gravity center algorithm. The identification algorithm distinguishes the infant’s posture change and sleep patterns by calculating these three kinds of parameters. Finally, the rating of infants’ sleep status is outputted. Compared with some wearable types of bracelet sleep monitoring facilities, the proposed system achieves the intellectualization, visualization and extendibility of information.

Moving Tracking Algorithm Based on Minimum Sigma Point Slope with Unscented Particle Reconstruction Filter

LIU Hong-qing, LIU Yan, SHU di-qing, ZOU zhi-xian

2019, 26(2):  355-361. 

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In order to improve the tracking accuracy of the moving object, a moving object tracking algorithm based on the minimum Sigma point slope particle reconstruction filter is proposed. Firstly, we use the split and merge tracker to solve the complex tracking problem under uncertain environment target tracking, the multi direction can be different from the position of the target parallel tracking, which could reduce the loss probability; Secondly, in order to reduce the computational burden due to the unscented particle filter Sigma point aggregation degree, we use the minimum Sigma point slope to improve the trace particle filter algorithm for the fission of sub trackers, which could improve the computational efficiency of the trace particle filter algorithm. Finally, the experimental results show that compared to the several selected location methods, the proposed tracking method can obtain better positioning effect at lower cost, and can balance the network load and improve the detectability of the lifetime of the sensor network.

Research on Load Power Prediction Model of Hybrid Power Ship

GAO Di-ju, PAN Kang-kai, WANG Tian-zhen

2019, 26(2):  362-367. 

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The short term prediction model of chaotic time series based on multi-resolution wavelet neural network (MRA-WNN) is set up to realize the optimal power allocation between the power sources of the hybrid power ship. The wavelet function and the scaling function are used as the network basis function. First, the overall profile of the time series is approximated in a large scale. And then, according to the different degrees of load power fluctuation, the approximation of the layer by layer is added to the details for improving the prediction accuracy. The translation and scaling parameters of wavelet basis functions are determined by the multi-resolution solution, and the number of training parameters can be decreased and the calculation speed can be improved by combining the multi-resolution analysis learning algorithm. The experimental results show that the MRA-WNN has high prediction accuracy, and it is an effective method for the prediction of the load power of hybrid power ships.

Network Filter Design for Continuous-time Systems Using a Novel Event-triggered Scheme

LI Jin-shuo

2019, 26(2):  368-372. 

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For the purpose of improving the usage of network bandwidth, and let the network filter be robust to packet drops happening during information transmission, a new algorithm of sampled event-triggered filter is proposed. Firstly, the structure of the network filter and triggered law is designed based on careful analysis of the nature of network environment. Secondly, utilizing the modeling method in the sampled-tine control system, the filter error system is modeled as a time-varying delay system with known bound. Using the Lyapunov-Krasovski method, a new sufficient condition for the existence of a H_∞ filter is given in the form of LMI. Finally, we give a simulation experiment to demonstrate the effectiveness of the proposed algorithm. The simulation results show that, compared with the traditional sampled-data filter, the proposed filter not only guarantees the H_∞ performance of the estimation error systems, reduces the traffic jam, but also has robustness of packet drops induced by the network environment.

Co-design of Control and Communication Based on Event-triggered Scheme for Networked Control Systems

WANG Zhi-wen, ZHAO Ying

2019, 26(2):  373-378. 

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Considering the coupled problems of communication network and control systems for network control systems, co-design of control and communication based on event-triggered scheme for network control systems is studied. First, the model of event-triggered control based on the communication network is built, by analysing the model of control systems and congestion control of the communication network, a novel event-triggered condition is presented. And then, by applying the Lyapunov-Krasovskii functional approach and linear matrix inequality, the performance of network control systems are analyzed, and the controller are designed. Finally, a numerical example shows the correctness of this conclusion.

Research on Fresh Logistics Monitoring System Based on Compressive Sensing

GENG Xiang-hua, WANG Gui-feng

2019, 26(2):  379-385. 

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The monitor system of the fresh logistics transportation system based on wireless sensor networks enables the real-time tracking and quality control of products. The traditional monitoring system needs to transmit the sensor data to the control center in real-time and it would cause the overload of data links and communication systems and reduction of data acquisition and transmission efficiency. This study aims to design a wireless sensor network monitoring system of fresh logistics transportation. By using the compressed sensing technology, the data sampling rate is reduced effectively to reduce the system pressure. Test results show that this system can accurately and efficiently recover the data collected by sensors and feedback the goods temperature variation throughout the fresh logistics in real-time to provide effective regulation and the security guarantee for the transport of goods.

Triangular Fuzzy Rules Based FANP Maintenance Decision Making for High Speed Railway Traction Power Supply System

LIU Hang, LI Qun-zhan, ZHAO Yuan-zhe

2019, 26(2):  386-392. 

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In order to improve the accuracy of the decision making process of high speed railway traction power supply, and to ensure the scientific and reasonable maintenance decision weight assignment, a new method based on triangular fuzzy rule for FANP maintenance decision making of high speed railway traction power supply is proposed. Firstly, the triangular fuzzy rules of classical network analysis method is improved, according to the fuzzy preference programming method, the index weight of high speed rail traction power supply is determined, involving weights of criteria, weighted coupling and affiliated index weight; Secondly, for constraints and coupling relation indexes, according to the network structure, the weighted super matrix is constructed and dealt with in the random way to obtain the limit form of the super matrix, and then the weight of each index of comprehensive is obtained, candidate for the weight score to choose the best maintenance method. Finally, the simulation test indicates that the FANP algorithm is better than the traditional maintenance decision-making plan and standard hierarchical network (ANP) scheme in the limit relative grade, dominance index, maintenance cost and time. Sensitivity analysis results of the algorithm are given.