The investigation on the rumor propagation model is of great significance in developing rumor-dispelling strategy and maintaining social stability. Considering the rumor propagation of college students on college network social platforms, the IG2D2 rumor model with six states is proposed, and then the rumor propagation model based on average field theory is established. Furthermore, taking the factors such as individual differences, conformity effect and trust degree into consideration, a novel non-consistent dynamical rumor propagation model is proposed. The simulation results show that the influence of the degree of the network on the peak value of rumor propagation decreases with the increase of conformity effect, and the retransmission degree of the rumor itself plays an important role in the propagation of rumor.

Research on product quality prediction for intermittent processes with limited data and nonlinear characteristics. In multi-source domain adaptation, the difference in the amount of data in the source and target domains leads to data imbalance, while there is nonlinearity in the process, which further leads to the inability to model accurately. To address this issue, the SMOTE method is first employed to eliminate the adverse effects of data imbalance on the model’s prediction results. Then, a nonlinear MDAJYPLS algorithm based on RBF networks is proposed to capture the nonlinear characteristics and make full use of the information from multiple similar source domains to assist the target domain process modeling. Further, a batch process quality prediction based on non-linear multi-source domain adaptive JYPLS via SMOTE is proposed, which can improve the prediction accuracy of the model. Finally, the effectiveness of the proposed method is verified by simulation experiments of penicillin fermentation process quality prediction.

To address the no-uniform distribution of alumina concentration in large-scale aluminum reduction cells, a feeding strategy based on adaptive distributed subspace prediction control is proposed. Firstly, the aluminum reduction cell is divided into multiple interconnected subsystems according to the spatial distribution of feeding ports, where adaptive distributed subspace predictive models for the alumina concentration of each subsystem are directly constructed from online data. Secondly, a residual-driven model switching mechanism is designed to trigger parameter updates only when model deviations exceed thresholds, thereby balancing computational efficiency and model accuracy. Finally, a distributed predictive controller based on Nash optimality theory is developed to achieve global coordinated feeding optimization. Simulation tests using real production data from an aluminum plant demonstrated that the proposed method significantly improved the spatiotemporal uniformity of alumina concentration in large-scale aluminum reduction cells.

There are problems in the optimization process of pelican optimization algorithm, such as the reduction of population diversity, the decrease of convergence speed, and the tendency to fall into local optimum. To solve these problems, multiple strategies are integrated to improve pelican optimization algorithm, and the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the pelican population is initialized by using the tent chaos map and refracted opposition-based learning strategy, which not only increases the population diversity, but also lays the foundation for improving the optimization ability of the algorithm. Then, a nonlinear inertia weight factor is introduced at the stage when pelicans approach their prey to improve the convergence speed of the algorithm. Finally, the leader strategy of salp swarm algorithm is introduced to coordinate the global search ability and local optimization ability of the algorithm. The improvement effect of the single improvement strategy is tested and IPOA is compared with 9 other optimization algorithms in the experiment. The experimental results prove the effectiveness of each improvement strategy and the superiority and robustness of IPOA.

For the limitation of existing control methods for obtaining intermediate states, a practical control method (stateless control) is proposed for suspension system by combining the linear quadratic regulator with the Luneberger observer. It directly establishes the relationship between current control input with historical control inputs and outputs. Using a quarter-car suspension system model as an example, the practical control performance of the stateless method is investigated. It incorporates sensor noise, modeled according to sensor precision, and accounts for suspension system parameter uncertainties. The results demonstrate that under both excitation and random excitation, the stateless control method effectively balances the three ride comfort performance indices of the suspension system, achieving excellent overall control performance. Crucially, the stateless control method only requires historical sensor outputs and control inputs to realize optimal control effectiveness even with limited sensors, thus significantly simplifying the control system design and offering significant practical value.

To maintain the stability of continuous stochastic systems driven by the Lévy process, a discrete control method is proposed. Firstly, a sliding mode controller is designed, which ensures that the system can maintain mean square exponential stability under Lévy noise interference by designing the control law reasonably. Secondly, the continuous controller is discretized to meet the requirements of actual digital control systems. Thirdly, through deduction, it is concluded that the second-order moment of the difference between
the system state under the action of a discrete controller and the system state under the action of a continuous controller is bounded, indicating that the discretization process does not significantly increase the instability of the system. Finally, the stability of the closed-loop system under the action of a discrete controller is demonstrated through theoretical analysis. The experimental results show that the discretized controller can still keep the system stable.

A cooperative output-feedback secure control scheme based on observers is proposed for connected and autonomous vehicle systems with intermittent denial-of-service (DoS) attacks on communication. Firstly, the dynamic model of the longitudinal connected and autonomous vehicle system is analyzed, and the feedback is linearized to obtain the linear dynamic equations. Secondly, by using the common Lyapunov function, a secure control scheme is designed to make the cooperative tracking error asymptotically stable. Finally, for maximizing the duration of DoS attacks, appropriate parameters are selected to design an optimization algorithm, in order to ensure the safe operation of the connected and autonomous vehicle system. The experiment simulated a networked vehicle system consisting of 4 followers and 1 leader, and the simulation results verified the effectiveness of the proposed method. The experiment is conducted by simulating a connected and autonomous vehicle system consisting of 4 followers and 1 leader. The simulation results verify the effectiveness of the proposed method.

To solve the problem that the conventional central pattern generator (CPG) introduce too many coupled dynamic parameters in their applications, which makes them difficult to adapt to the robots with insect-like leg structure, an improved CPG control method combined with inverse kinematics to realize foot-end control is proposed. Firstly, at the system level, a linear converter, a function generator and an inverse kinematics solution module are designed, and the conventional CPG application is parametrically improved in a foot-oriented control manner. Secondly, in terms of details, the limit cycle of CPG is improved to enhance its adaptability to terrain. Finally, functional gaits such as linear steering are planned based on the improved CPG model, and the closed-loop control for attitude stabilization based on foot end deviation compensation is achieved. The experimental results show that the proposed method simplifies the gait control, enabling the insect-like hexapod robot to achieve linear steering on undulating terrain and maintain stable attitude.

The basis for predictive control of air conditioning terminal systems in data centers is the multi-step prediction of cabinet inlet temperature. To improve the precision and the portability of the prediction model, a nonlinear Takagi-Sugeno (T-S) fuzzy method is proposed to construct the temperature prediction model of data center server rooms. Firstly, the computational fluid dynamics (CFD) model of a server room is developed by using CFD numerical simulation method, and a data acquisition strategy is designed to capture the complete dynamic characteristics of the system. Then, to solve the problem that the fuzzy C-mean clustering algorithm is prone to local optimum, the improved beetle antennae search algorithm is proposed to optimize the identification of the forepart structure of the T-S fuzzy model. Finally, the cubature Kalman filter is adopted for the posterior part parameter identification and online correction of the T-S fuzzy model. The experimental results show that the T-S fuzzy model constructed by this method has higher computational efficiency and prediction accuracy than that of the conventional T-S fuzzy model, and the requirement of model portability can be met through the update of the posterior part parameters.

The problem of memoryless feedback stabilization for a class of linear systems with unknown input delays is studied. For a class of unstable linear systems whose open-loop poles are located on the imaginary axis, a finite-dimensional memoryless feedback control scheme is proposed when the input delay is unknown. By designing a memoryless truncated predictor feedback (TPF) controller, the implementation problems brought by traditional infinite-dimensional control schemes are avoided. When the unknown input delay is in a time interval, the global asymptotic stability of the closed-loop system is guaranteed. An explicit expression for the variation range of the unknown input delay is given. Finally, the designed memoryless truncated predictive feedback controller is applied to the delayed dual oscillator system and anti-pitching control of high-speed catamaran, and its effectiveness is verified through MATLAB.

In the control of bronchoscopic robots, the accuracy of the conventional proportional integral differential (PID) control is insufficient, and the back propagation (BP) neural network is prone to fall into local optimum. To solve the problems, a BP neural network-PID control method optimized by the improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the conventional whale optimization algorithm, IWOA introduces a nonlinear convergence factor to dynamically balance the global search ability and local search accuracy, optimizes the population distribution by using tent chaotic mapping, enhances global optimization by using the Lévy flight strategy, maintains population diversity by combining the greedy selection mechanism, and provides the optimal initial connection weights for the BP neural network. Then, the BP neural network fuses the reference input, system output and tracking error at the input layer, and dynamically adjusts the PID control parameters through backpropagation. The simulation results show that, compared with PID control, BP neural network-PID control and their improved methods, the proposed method can significantly reduce the overshooting of the system, shorten the regulation time, and make the steady-state error approach zero. This method has good control accuracy and anti-interference ability, so it can significantly reduce mechanical vibration and tissue friction during operation, and improve the safety of bronchoscopic surgery.

The existing image inpainting models are unable to inpaint images with large-scale defects at a high quality. To solve this problem, an image inpainting model based on Transformer and the generative adversarial network is proposed. Firstly, a mask adapt input module is designed, which is used to extract the image blocks that are not masked from the input image. Secondly, the Transformer is used to extract global context information from the valid image blocks, thereby enhancing the model’s ability to inpaint missing areas. Thirdly, the fast Fourier convolution (FFC) modules are used to enhance the ability to inpainting details and eliminate artifacts in the output image. Finally, the performance of the whole network is improved under the adversarial training of discriminator network. The proposed model is used to inpaint the images of Place2 dataset. The test results show that when the mask ratio is 50%~60%, the peak signal-to-noise ratio of the inpainting results reaches 19.748 2 dB, and the structural similarity (SSIM) reaches 0.714 7.

Accurate trajectory tracking is the key for the intelligent vehicle to achieve autonomous motion control. A novel robust adaptive sliding mode control strategy is proposed to dealing with the issue of system uncertainty affecting the accuracy of trajectory tracking control. Firstly, a two-degree-of-freedom vehicle dynamics model is established based on the principles of vehicle kinematics. Secondly, a sliding surface of proportional integral derivative (PID) type with adaptive properties is designed based on trajectory tracking errors, and the accuracy and robustness of trajectory tracking control are improved by designing adaptive update laws to estimate the sliding mode control gains and the upper bound of system uncertainty in real-time. Thirdly, the control parameters of the controller are optimized by the particle swarm optimization algorithm, which further improves the trajectory tracking control performance. Finally, the proposed control strategy is verified by the simulation under different road conditions and vehicle speeds. The simulation results show that the proposed control strategy can ensure that the intelligent vehicle tracks the target trajectory under the influence of system uncertainty, and its control performance is superior to the fractional order PID control.

In order to address issues of low simulation accuracy, poor universality and poor visualization effect, a universal high-fidelity air-to-air missile simulation system based on Unity3D and CADAC is designed. On the one hand, the designed simulation system uses a high fidelity CADAC software package, which comprehensively considers the missile kinematics and aerodynamics, different guidance laws and target maneuvering modes, fuel consumption and center of gravity changes, and other factors, thus improving the fidelity of the simulation system. On the other hand, the designed system can be applied to different category of missiles such as air-to-air missiles, air-to-ground missiles, etc., which improves the universality and adaptability. Finally, Unity3D is used to conduct visual simulation, and visually display the whole process from launching, searching, guidance and hitting the target. Typical simulation cases and performances analysis is given to verify the effectiveness of the simulation system.

To address the challenge of achieving low-power consumption and rapid online measurement of chemical oxygen demand (COD) in small-scale wastewater treatment plants, an online self-organizing neural network (OSNN) prediction method based on radial basis function (RBF) is proposed. This method realizes accurate prediction of COD by dynamically controlling the number of neurons and their update rate. By leveraging the excellent continuous function approximation capability of RBF, combined with the flexibility and adaptability of self-organization, the accuracy and adaptability of the measurement model are improved. The proposed method for controlling the update rate of neuron number can maintain the compactness of the neural network and reduce the extension of training time caused by frequent and substantial changes in neuron number. Experimental results demonstrate that the RBF neural network with self-organizing capability can reliably predict the COD parameter values.

The steam specific enthalpy and dryness of steam turbines in small-scale cogeneration units are important indicators for evaluating turbine safety and economy. However, they are usually not measurable online. An approach for online calculation of these parameters is proposed. First, the superheated state of the steam at the outlet of the steam turbine stage is determined. For the superheated steam, the specific enthalpy of the stage outlet steam is calculated by solving the isentropic enthalpy drop and internal efficiency of the stage. For the saturated steam, the specific enthalpy and dryness of the stage outlet steam are calculated by means of a comprehensive calculation model incorporating the isentropic enthalpy drop, internal efficiency and saturated steam specific enthalpy. Verification results for a 15 MW cogeneration steam turbine demonstrate that the proposed approach is feasible for online application and achieves high precision.

Simultaneous localization and mapping (SLAM) is a key problem in the research and application of mobile robots, which is used to realize autonomous and accurate localization of mobile robots in complex environments. The system composition, key technologies and applications of SLAM are briefly introduced. Focusing on five aspects: feature point method, filtering method, graph optimization method, multi-sensor fusion and dynamic scene, the key technologies, domestic and foreign research status and symbolic application progress of SLAM system are reviewed. Combined with representative systems, the advantages and disadvantages of different methods are compared and analyzed, and the multi-sensor fusion SLAM systems are elaborated in detail, and the SLAM technology in complex scenes is prospected.

In actual industrial production, the insufficient number of fault samples for rolling bearings leads to inaccurate fault diagnosis. To solve this problem, a fault diagnosis method based on the Glow-ECNN model is proposed by combining the convolutional neural network model with the efficient channel attention (ECA) mechanism (ECNN model) and the generative flow (Glow) model. Firstly, one-dimensional fault vibration signal is transformed into a two-dimensional time-frequency image that contains time-frequency feature information by continuous wavelet transformation. Then, the time-frequency images are input into the Glow model for data augmentation, generating a sufficient number of time-frequency images with a similar distribution to the original ones. These generated time-frequency images are combined with the original time-frequency images as training samples. Finally, the ECNN model is used to classify the faults. The experimental results show that the proposed method can achieve an accuracy rate of 99% in fault diagnosis of rolling bearings under small sample conditions, demonstrating its feasibility and effectiveness.

Intelligent underwater robots are prone to malfunctions, which can affect underwater operations. Propose a fault-tolerant control method based on adaptive reinforcement learning. This method introduces the Actor-Critic algorithm, which learns and formulates action strategies through the Actor network, while the Critic network evaluates the value of actions and adaptively adjusts strategies based on external environmental changes. Meanwhile, an improved extended state observer based on integral mechanism was designed, and an anti integral saturation algorithm was adopted to avoid integral saturation. The simulation results show that when the thruster of the intelligent underwater robot fails, the error values of the proposed fault-tolerant controller in the x-axis and y-axis directions gradually approach 0 after 15 seconds, proving that the designed fault-tolerant controller has excellent fault-tolerant performance and stability, and can provide effective technical support for the safe operation of underwater intelligent robots.

The aircraft anti-skid braking system determines the safety and comfort of the aircraft landing process. Firstly, a dynamic analysis of the aircraft anti-skid braking system is conduct, and a model of the aircraft anti-skid braking system is established. Then, to solve the problem that the integrator in the conventional integral sliding mode control has the phenomenon of integral saturation, by setting the slip rate as the control target, the integral sliding mode surface is improved, and the controller is designed and verified through simulation. The simulation results show that the improved integral sliding mode controller can enable the slip ratio to quickly track the target slip rate with strong robustness, effectively reduce the output buffering of the controller, and improve the performance of the aircraft anti-skid braking system.

A predictive sliding mode control method based on Kalman filter is proposed to improve control performance of predictive sliding mode control method for systems with model mismatch, random interference and control input saturation. The unmatched information expressed in the form of a parametric covariance matrix by treating the model mismatch as interference is regarded as the process noise of system together with the external white noise interference. The sensor measurement error is taken as a part of measurement noise of the system. Kalman filter is used to estimate the disturbed state variables and applied to controller design. Finally, the experimental results of the inverted pendulum demonstrate the effectiveness of the proposed method.

Cascade control systems have stronger anti-disturbance ability and adaptive ability than single-loop systems, and therefore are widely used in industrial systems, most of which are non-Gaussian systems. Entropy is often used as the performance index to assess the performance of non-Gaussian systems, but the entropy index has the problem of translation invariance. Therefore, the minimum entropy index is weighted and mixed with the Jensen-Shannon (JS) divergence index, and a mixed assessment index is proposed. The performance assessment of non-Gaussian systems requires the effective identification of system parameters based on closed-loop data. In order to obtain the accurate reference, based on the ideas of particle swarm optimization algorithm and estimation of distribution algorithm, and combining the advantages of both, a hybrid optimization algorithm is proposed for system parameter identification. Finally, the cascade control system is simulated under different non-Gaussian noise disturbances. The simulation results verify the effectiveness of the proposed hybrid optimization algorithm and hybrid assessment index.

Industrial process data have characteristics such as high dimensionality and imbalance, which can affect the accuracy of industrial process performance evaluation. To solve this problem, a multi-data space integration model of least squares support vector machine (LSSVM) based on latent variable technology and particle swarm optimization (PSO) algorithm is proposed for industrial process performance evaluation. Firstly, the sampled process variable data are divided into different data space according to performance levels. Then, feature mapping is performed on the data spaces of different performance levels to extract latent variables, and latent variables are screened by mutual information to achieve the goal of reducing the dimension of data spaces. Finally, sub-models of LSSVM are established in different data spaces, and PSO algorithm is used for their integrated optimization to obtain the offline model. The offline model obtains the performance evaluation results by calculating the similarity between the online data and each performance level. The proposed method is applied in the simulation of the operation performance evaluation of the ethylene cracking furnace, and the simulation results prove its effectiveness.

In order to improve the response speed and robustness of the AC microgrid inverter output voltage, a performance improvement control strategy combining linear quadratic optimal control and residual generator is proposed. Firstly, the state space model of the three-phase voltage inverter is established, and a quadratic optimal controller with a fast response is designed based on the model. Secondly, according to the double coprime decomposition and Youla parameterization theory, a performance improvement control structure based on the residual generator is obtained. Thirdly, based on the model matching principle, the performance improvement controller is solved to improve the robustness of the system. Finally, several experiments of step disturbance, three-phase imbalance load and nonlinear load are designed, and the experimental results verify the effectiveness of the proposed control strategy.

In order to improve the efficiency of transmission line selection and reduce the construction cost of transmission lines, an improved ant colony optimization algorithm based on geographic information system is proposed. Firstly, a raster model of the planned area is established to expound the application principle of the conventional ant colony optimization algorithm in the transmission line selection. Then, to solve the problems that the conventional ant colony optimization algorithm is prone to fall into local optimum and the searched path has many inflection points, an adaptive update mechanism of pheromone concentration and a node optimization mechanism are proposed to improve it. The experiment takes a certain area in Anhui Province as an example to select the transmission line. The experimental results show that, compared with the conventional ant colony optimization algorithm, the improved ant colony optimization algorithm has higher search efficiency, and the searched path has fewer inflection points, which can effectively reduce the construction cost of the transmission line.

Taking a six-degree-of-freedom (6-DoF) serial manipulator as the research object, the concept of key joints is first introduced. By implementing torque control at these key force-controlled joints while simultaneously incorporating their output angles into the manipulator’s position control, a hybrid force/position control algorithm based on active force-controlled joints is proposed. Leveraging the theories of manipulator dynamics and inverse kinematics, the proposed algorithm achieves force control along a predefined positional trajectory. Finally, for the RM65-B dexterous 6-DoF manipulator, a MATLAB-based simulation platform is established to conduct experiments and analyze the tracking performance of the hybrid force/position control scheme. The favorable simulation results validate the feasibility of the proposed control method.

The manipulator system has problems such as unmodeled part, friction, and external disturbance. Therefore, an integral fast terminal sliding mode control method considering input saturation is proposed for the trajectory tracking control of joint angles. Firstly, the manipulator model is established, and regard the unmodeled part, friction, external disturbance, and input saturation error as are regarded as concentrated disturbance. Secondly, a fixed-time extended high gain observer is designed to estimate the concentrated disturbance, which solves the problem that the estimation error of the conventional extended high gain observer has a peak at the initial moment and can only be asymptotically stable. Thirdly, the integral sliding mode control is combined with the fast terminal sliding mode control, and the backstepping method is used to design the integral fast terminal sliding mode controller. The buffeting of the control output is reduced by the concentrated disturbance estimated through the observer. Finally, simulation experiments are conducted on the proposed observer and control method. The simulation results show that the proposed observer can accurately estimate the concentrated disturbance, and the proposed control method can improve the trajectory tracking speed and accuracy of the manipulator system, ensuring the safety of the manipulator system while considering the input saturation.

In recent years, inspection robots have greatly improved the safety of mine operations. However, the unstructured environment of mines poses difficulties in the high cost and maintenance to track inspection robots, and the explosion-proof requirement of mines limits the hardware design of inspection robots. A mine inspection unmanned aerial vehicle (UAV) system that meets intrinsic safety standards is proposed for the first time, and designs low-power UAVs and charging ground cabins for the explosion-proof requirement. In addition, the UAV inspection scheme is adopted to overcome the difficulties caused by the unstructured environment of mines. Furthermore, a scheduling strategy model for unmanned inspection tasks in underground mines based on automata is established, and synthesized a complete scheduling strategy for unmanned inspection tasks in underground mines based on the supervised control theory. Finally, the effectiveness of the scheduling strategy is verified through scheduling experiments with a UAV and a ground handling cabin.

PID control algorithm is a widely used control strategy with reliable control performance, and it has many advantages such as simple structure, easy tuning and easy implementation. In order to solve the parameter tuning problem of PID controller with robustness constraint, we apply non-dominated sorting genetic algorithms-II (NSGA-II) to tune the PID controller parameters and applies the optimized PID to the control of flight attitude and altitude of quad-rotor aircraft. Firstly, the quad-rotor aircraft model is linearized. For the altitude and attitude channels of full drive control, the linear model of the sub-channel is obtained. Then, in the NSGA-II, PID parameters are selected as decision variables, the rejection ability performance and tracking performance are taken as the two objective functions, and the maximum sensitivity function is taken as the robustness constraint. Finally, by comparing the parameter control performance of the simulations with that of the traditional method, the superiority of multi-objective genetic algorithm tuning PID in tracking and jamming is verified.

To solve the problems of low accuracy and poor generalization ability of the prediction model caused by outliers or noise in data, a robust prediction interval method based on stochastic configuration network and Bayesian quantile regression is proposed. Firstly, the stochastic configuration network (SCN) algorithm is employed to determine the number of the nodes in the hidden layer, as well as the input weights and biases. Then, the Bayesian quantile regression is embedded into the SCN to replace the classical least squares regression, the asymmetric Laplace distribution is used as the prior distribution of the SCN noise, and the maximum posterior estimation is used to convert the prior distribution of the SCN noise into the posterior distribution of the output weights. Finally, the expectation maximization algorithm is used to iteratively optimize the SCN noise and hyper-parameters of the hypothesis distribution on the output weights. The experiment is conducted based on the standard datasets and historical data of the municipal solid waste incineration process to test the proposed method, and it is compared with other prediction algorithms based on SCN and quantile regression. The experimental results show that the proposed method has advantages in terms of the accuracy and generalization ability of point predictions, the reliability of prediction intervals, robustness, and computational efficiency.

For the lot-sizing scheduling problem of uncorrelated parallel machines considering the adjustment time of sequence-dependent machines, a mathematical model of the problem is established with the optimization objectives of minimizing the maximum completion time (Makespan) and the total number of machine switching times, and an improved multi-objective biogeography optimizer (IMOBBO) is designed to solve the problem. In order to meet the needs of lot-sizing scheduling, a partitioned matrix coding method is designed in the first stage, and the initial population is generated by the fusion strategy of random strategy, Logistic mapping and reverse learning mechanism, and wandering mechanism of wolves are introduced into the process of species migration, adaptive catastrophe operator and two-stage neighborhood search strategy are used in the catastrophe process; in order to minimize the adjustment time of each machine, the second stage adopts machine-based matrix sequence coding to optimize the processing sequence of each batch, and finally, the individual evaluation method based on hypervolume is introduced to Pareto non-dominated rank ordering. The effectiveness and superiority of the algorithm proposed are proved through simulation experiments of different scales and examples and comparison with related algorithms.

To address the difficulties in stochastic distribution control teaching, including high theoretical abstraction, the gap between simulation and real industrial constraints, and limited experimental resources, a pure physical experimental system based on a vertical disc grinding process was developed. The system integrates platform construction, data acquisition, modeling, and control into a unified framework. Centered on a self-developed vertical disc mill, it incorporates precise feeding, disc gap adjustment, and speed regulation, while real-time particle size distribution data are obtained through automatic intermittent sampling and a dry laser particle size analyzer. Radial basis function expansion and inverse integration are used to parameterize the output probability density function. Combined with iterative learning and subspace identification, a linear prediction model describing the dynamic relationship between manipulated variables and weight vectors is established. A constrained controller is further designed to achieve target distribution tracking under input constraints and disturbances. Experimental results show that the system is stable and interactive, and can effectively support experimental teaching in stochastic distribution control and related courses.

For the filtering problem of nonlinear systems with Gaussian noise and non-Gaussian noise, if the mixed noise is treated as non-Gaussian noise, the filtering accuracy will be affected by ignoring the characteristics of Gaussian noise. Therefore, based on the idea of “system split + algorithm fusion”, a new non-Gaussian nonlinear filtering algorithm is designed. Firstly, the system split weights and introduced to divide the nonlinear system under the mixed interference of multiple noise into several subsystems affected by a kind of noise. Then, according to the noise characteristics of each subsystem, the corresponding sub-filtering algorithm is designed. Finally, the filtering results of each sub-filtering algorithm are fused. Additionally, two kinds of weights, namely average weights and dynamically updating weights, are introduced. The simulation results show that, compared with the existing nonlinear filtering algorithms which regards the mixed noise as a kind of Gaussian noise or non-Gaussian noise, the proposed algorithm has a significant advantage in filtering accuracy.

Process monitoring based on multivariate statistical analysis requires large amounts of historical data for modeling. The presence of outliers in the data can affect the accuracy of the modeling, thus reducing process monitoring performance, even increasing the false alarm rate and the missing alarm rate. A robust process monitoring method based on artificial neural network (ANN) and canonical correlation analysis (CCA) are proposed to deal with the outlier issue in the data. The method is divided into two phases: offline training and online monitoring. In the offline training phase, the expanded Kurtosis is chosen as the loss function of ANN according to the principle that the outliers obey the sub-Gaussian distribution to eliminate the influence of outliers; in the online monitoring phase, outliers are first rejected based on the difference in probability of consecutive occurrence of outliers and faults, and then process monitoring is performed using CCA. To verify the effectiveness of the proposed method, simulation tests are performed on a numerical example and continuous stirred-tank reactor. The results show that the proposed method can effectively eliminate the effect of outliers, improve the fault detection rate, and reduce the false alarm rate.

For the consensus control problem of second-order multi-agent systems with external disturbances in undirected topology, a distributed fixed-time control protocol based on sliding mode technology is proposed. Firstly, a second-order distributed fixed-time observer is designed to realize the accurate estimation of the leader’s state information in fixed time for each follower. Then, based on a novel distributed fixed-time sliding surface designed with system state errors, a distributed fixed-time consensus control protocol considering external disturbances is proposed. The control protocol not only effectively improves the convergence rate of the system, but also ensure that the system state tracking error reaches zero within the fixed time. At the same time, the stability of the closed-loop system is proved by Lyapunov theory. Besides, the upper bound of the stability time can be clearly estimated according to the control protocol parameters within unknown initial state of the system. Finally, the correctness and validity of the theoretical analysis results are verified by numerical simulation.

The current elevator group control scheduling system has insufficient debugging safety, high difficulty in design evaluation, and high debugging costs. Therefore, a simulation model for elevator group control is developed. Based on this model, the elevator group control scheduling scheme is dynamically evaluated according to indicators such as the average waiting time of passengers, long-time waiting rates, and the energy consumption of the elevators. To address the issues of low elevator operation efficiency and poor passenger comfort during elevator rides, a deep Q network (DQN) is developed to realize optimized scheduling for elevator group control. The corresponding state space, reward signals, and agent structure are designed by the primary factors affecting elevator transportation efficiency. Considering the lengthy duration of online training for reinforcement learning agents and the challenge of providing real-time decision support, the operational data from the elevator group control simulation model is utilized to train a feedforward neural network, and an elevator group control environment prediction model is developed to serve as the agent training environment. Simulation experiments are conducted by using the elevator group control simulation model. The results showed that, compared with the prevalent minimum response time strategy, the proposed strategy reduces the average waiting time of passengers, average time that passengers spend in the elevator, long-time waiting rate, the number of elevator starts and stops, and increases the average number of arrivals within 5 minutes.

Aiming at the problem of positioning and anti-swing control of bridge crane system, a parallel control method based on data driven model free adaptive control and nonlinear PID control with tracking differentiator (TD) is proposed. The dynamic linearized data model of the bridge crane system is given based on the displacement output and the control input signals of the system. A model-free adaptive control (MFAC) method is designed on the base of the virtual model. Considering that the model-free adaptive control method is sensitive to system disturbance data and requires high data accuracy, the dual TD nonlinear PID control is paralleled with the model-free adaptive control and the system stability and error convergence are proved. The simulation results show that this compound control method not only realizes the accurate positioning of the trolley and the suppression of the load swing angle, but also reduces the influence of the disturbance on the system and improves the robustness of the system.