This paper investigates a formation tracking control problem of heterogeneous multi-agent systems (MASs) with unknown nonlinear dynamics and asymmetric full-state constraints under fixed and switched communication topologies. Firstly, fuzzy logic systems are employed to approximate unknown nonlinear dynamics. Then, a distributed observer is developed to estimate the state information of the leader. By using the high-order fully actuated system approach, a nonlinear mapping function is introduced to transform an original constrained system into an unconstrained high-order fully actuated system model, effectively reducing computational complexity. Based on this transformed model, an adaptive formation controller is designed for each follower. Theoretical analysis shows that the designed formation controller guarantees that all system states remain within a specified constraint set, while achieving the desired formation performance of heterogeneous MASs under fixed and switching topologies. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control strategy.

This paper proposes a distributed adaptive optimal fault-tolerant formation containment control scheme for high-order nonlinear multi-agent systems (MASs) subject to actuator faults and unknown dynamics. Radial basis neural networks are employed to approximate unknown nonlinear functions. Subsequently, a distributed extended observer is designed to estimate unmeasurable system states. Based on the fully actuated system (FAS) approach, an adaptive reinforcement learning algorithm is developed within an identifier–critic–actor framework to handle high-order dynamics and derive the control input, thereby achieving nearly optimal control. Moreover, an (n-1)-order sliding mode surface is constructed to regulate the tracking error, and an adaptive optimal fault-tolerant control strategy is developed. Theoretical analysis shows that even in the presence of actuator faults, MASs can maintain their output within the convex hull formed by the leaders, while achieving high precision formation trajectory tracking and optimized overall performance. Finally, the feasibility and effectiveness of the proposed control scheme are verified through a simulation example.

This paper addresses the distributed resilient optimal sliding-mode consensus control of constrained vehicle platoons subject to intermittent denial-of-service (DoS) attacks and input delays. Radial-basis-function neural networks are adopted to approximate unknown system nonlinearities. First, a distributed switched observer is constructed to identify attack activation states, which enables attack-dependent switching logic under communication interruptions. Second, an adaptive sliding surface integrated with a tangent-type barrier Lyapunov function is designed to strictly enforce the vehicle platoon’s state safety constraints. This sliding surface is equipped with robust compensation and cooperative recovery components to sustain closed-loop stability during active DoS attacks. To balance tracking precision and control energy consumption, an actor-critic adaptive dynamic programming framework is embedded to online solve the optimal control problem. The cost function introduces an attack-aware penalty term to counteract performance losses induced by communication interruptions, while sliding-mode regulation provides inherent anti-disturbance robustness. Rigorous Lyapunov analysis proves all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). The tracking errors converge to a small residual neighborhood of zero, and the proposed framework simultaneously satisfies predefined state constraints, mitigates intermittent DoS attacks, and compensates powertrain input delays. Numerical simulations on strict-feedback pendulum systems and a longitudinal vehicle platoon verify the theoretical validity and practical applicability of the developed control scheme.

This paper investigates predefined-time optimized consensus control problems for nonlinear multi-agent systems suffering from actuator faults, in which unknown nonlinearities are approximated by radial-basis-function neural networks. Firstly, an adaptive reinforcement learning algorithm is applied to solve the optimal control issue under a critic-actor framework. Secondly, a novel predefined-time filter is constructed and incorporated into the optimal dynamic surface control scheme to avoid repeated differentiation of virtual control laws. Then, a dynamic event-triggered mechanism is designed in the controller-to-actuator channel to save communication resources. Moreover, this paper also considers fault types of actuator efficiency loss and bias, and uses adaptive technology to effectively compensate for actuator faults. The results show that the tracking error can be steered to a small neighborhood of the origin within a settling time, and all signals in the closed-loop system are uniformly ultimately bounded. Finally, the effectiveness of the proposed control method is verified by a simulation example.

This article investigates an event-triggered optimal time-varying formation (TVF) control problem for nonlinear multiagent systems with unmeasurable states and unknown nonlinear dynamics. A neural network-based state observer is first designed to reconstruct unmeasurable system states. Meanwhile, neural networks are employed to approximate unknown nonlinear functions. Subsequently, an adaptive reinforcement learning algorithm is proposed to address the optimal control issue within an identifier-critic-actor framework. To save system communication resources, this article designs a dynamic event-triggered mechanism in sensor-controller channels and develops an adaptive state event-triggered control scheme. Unfortunately, state-triggered control may cause nondifferentiability in virtual control laws, posing significant challenges for the design of an optimal TVF controller. To overcome this obstacle, we use observer output signals to construct virtual control laws. Then, an event-triggered controller is constructed by replacing estimated states with intermittent estimates. The results show that formation tracking errors can converge to a small neighborhood near the origin, and all closed-loop signals are semi-globally uniformly bounded. Finally, the effectiveness of the control scheme is verified by an example simulation.

Under the framework of backstepping theory, dealing with the non-differentiable problem of virtual control signals caused by sensor output triggering is difficult. Meanwhile, it is of great practical significance to consider problems of output triggering, multiple faults, and denial-of-service (DoS) attacks in nonlinear multi-agent systems (MASs). This paper studies a neural-network-based event-triggered adaptive secure fault-tolerant containment control problem for nonlinear MASs under multiple faults and DoS attacks. Under sensor output triggering, only intermittent output signals are used to construct a switched neural network estimator to guarantee that estimated states are first-order derivable. Meanwhile, virtual control laws are constructed using estimated states to ensure first-order differentiable, and dynamic filtering technology is adopted to avoid the repeated differentiation of virtual control laws. It is shown that the designed secure fault-tolerant containment controller can compensate for faults and DoS attacks, and each follower can converge to a dynamic convex hull spanned by multiple leaders. Practical simulation results are given to verify the effectiveness of the proposed control method.

In this study, an interval-type-2 (IT-2) fuzzy controller for nonlinear networked control systems (NNCSs) under multichannel denial-of-service (DoS) attacks is designed. First, an NNCS is modeled as an IT-2 fuzzy control system, and uncertainty is introduced into the NNCS. Second, DoS attacks are performed simultaneously in the sampler-to-controller channel and the controller-to-actuator channel. The DoS attacks are modeled via the Bernoulli distribution. Owing to the limited performance of the actuator and because DoS attacks introduce large amounts of useless data, the actuator needs to process large amounts of data; thus, this study considers the actuator saturation problem. Finally, to alleviate the pressure of channel transmission data and improve the utilization of network resources, an improved adaptive event-triggered mechanism (AETM) is proposed. Considering the above situation, a closed-loop system is established, and an IT-2 fuzzy controller is designed for the system. The simulation results for the IT-2 fuzzy control system prove the validity of the controller design proposed in this paper.

This paper focuses on the distributed event-triggered adaptive neural resilient time-varying formation control problem for a class of multiple-input multiple-output nonlinear multi-agent systems, where all network communication links between agents are subjected to denial-of-service (DoS) attacks simultaneously. A second-order resilient time-varying formation estimator is designed to obtain the unknown leader information in DoS attack active intervals. Meanwhile, a state-triggering mechanism (STM) is designed to save system communication resources. Nevertheless, the STM can lead to virtual control laws being non-differentiable. To circumvent the problem, we first design an adaptive neural resilient formation control scheme. Then, based on the adaptive neural resilient formation control scheme, we replace continuous states with intermittent ones. By utilizing a dynamic filtering technique, an event-based adaptive neural resilient formation control scheme is designed. The key technology of control scheme design is to establish an improved first-order auxiliary system to deal with the negative impact of actuator saturation. It is proved that formation tracking errors can converge to a residual set around zero, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the control scheme.

This paper is concerned with the problem of dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for multi-agent systems subject to actuator saturation, in which state variables are unmeasurable and nonlinear functions are totally unknown. A fuzzy state observer is constructed to estimate unmeasurable states. Meanwhile, fuzzy logic systems are used to approximate unknown nonlinear functions. To effectively save the usage of communication resources, this paper designs both sensor output signal and control signal triggering mechanisms, respectively. Unfortunately, the output triggering can lead to a problem that virtual control laws are non-differentiable. To solve this problem, we first utilize observer output signals to construct virtual control laws to ensure the first-order differentiation of virtual control laws. Then, a dynamic filtering technology is introduced to avoid the repeated differentiation of virtual control laws. Furthermore, an improved first-order auxiliary system is designed to compensate for the impact of actuator saturation. It is shown that the designed controller can guarantee tracking errors steer to a preset accuracy within a prescribed settling time, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results verify the effectiveness of the developed control scheme.

This paper studies the event-triggered time-varying formation control problem for nonlinear multi-agent systems with actuator faults. Based on the neural network approximation technique, a neural observer is constructed to estimate the unmeasured states of systems. Then, a distributed adaptive event-triggered time-varying formation control manner is proposed utilizing the intermittent estimated states information from the agent and its neighbors. To overcome the problem that estimated states triggering leads to virtual control laws is non-differentiable, a distributed continuous control scheme under regular output-feedback is designed firstly, upon which a distributed event-triggered controller is constructed by replacing estimated states with intermittent estimated ones. It is shown that the designed event-triggered output-feedback time-varying formation fault-tolerant controller can compensate for actuator faults, and all signals in closed-loop systems are semi-globally uniformly ultimately bounded. Finally, simulation results of a practical example are given to verify the effectiveness of the proposed control manner.

This paper investigates the problem of predefined-time event-triggered adaptive tracking control for strict-feedback nonlinear systems (SFNSs) with full-state constraints. To handle asymmetric full-state constraints, a nonlinear state-dependent function (NSDF) that purely depends on the constraint state is introduced. Then, a switching threshold event-triggered mechanism (ETM) is designed to enhance usage efficiency of communication resources. Moreover, a predefined-time event-triggered adaptive tracking controller is constructed by incorporating a smooth tuning function into each step of the backstepping design based on dynamic surface technology. Under such a control scheme, the output tracking error can be steered to the small neighborhood of the origin within the user setting time. Compared to various previous control schemes developed for full-state constrained systems, our proposed method can circumvent the demanding feasibility conditions in controller designs. Finally, a simulation result is given to illustrate the effectiveness of the control scheme.

This paper studies the periodic event-triggered bipartite containment control problem of multi-agent systems with unmeasurable states and input delays. To deal with the system input delay problem, a Pade approximation technique is introduced. By utilising the sampled output of the system, a fuzzy state observer is constructed to estimate the unknown state. A periodic event-triggered mechanism is designed, which can effectively reduce the usage of communication resources. Compared with the traditional event-triggered control manner, a periodic event-triggered control manner only monitors the preset threshold condition at the sampling instant to determine whether to update the current control signal, which can automatically guarantee the minimum lower bound of the execution interval, avoiding the occurrence of Zeno behaviour. Stability analysis verifies that all signals in the closed-loop system are bounded, and the bipartite tracking error can converge to a small neighbourhood of the origin. Finally, a practical example further verifies the effectiveness of the designed control scheme.

针对学生对12脉波大功率相控整流电路理解上的难点,利用Simulink工具箱建立了12脉波相控整流电路的仿真模型.详细说明了带平衡电抗器的12脉波相控整流电路仿真模型的建立过程,分别测试了在有、无平衡电抗器情况下,不同触发角时,整流电路的输出特性.所建立的仿真模型有利于加深学生对于整流电路中平衡电抗器作用和2组并联整流桥负载的均衡原理等核心知识点的理解,可以有效弥补实验设备不足、实验操作不安全等问题.

为了对双反星形大功率可控整流电路进行直观准确地分析,在MATLAB环境下,采用Simulink工具箱建立了双反星形可控整流电路的仿真模型.详细说明了带平衡电抗器的双反星形可控整流电路的仿真模型的建立过程,测试了在存在平衡电抗器时,电路带电阻性、电感性负载时的输出特性.为了对比进行说明,同时建立了三相半波可控整流电路的仿真模型.仿真结果表明,用Simulink工具箱构建电路系统的仿真模型方法简单,双反星形整流电路更适合于在低电压大电流领域中应用.

In view of the shortcomings of wind turbines gearbox fault diagnosis technology, this paper presents a diagnosis method based on self-organizing feature mapping (SOFM) neural network. First denoised the vibration signals of a wind turbine gearbox in its normal state, wear fault and tooth breakage through wavelet analysis method. Then five fault feature indexes in time domain and frequency domain were taken as input eigenvectors to train the network. And diagnosed the fault type according to the location of output neurons on output layer. At last a fault diagnostic model based on SOFM neural network was built. In order to test its diagnostic ability, the built model was used to diagnose the measured data of wind turbine gearboxes of a wind farm in northern China. The simulation results show that the built model can judge the fault type according to the location of winning neurons in the competing layer. And its diagnosis accuracy is high; its convergence speed is fast and its generalization ability is also good. It is indicated that the established network model can effectively diagnose gearbox fault.

To overcome the low accuracy of traditional transformer fault diagnosis methods, a new method is proposed combining analysis of dissolved gas (DGA) in oil with discrete Hopfield neural network (DHNN). The eigenvectors of transformer fault diagnosis were obtained via improved Rogers three-ratio method, then a fault diagnosis model of the discrete Hopfield neural network was established. To further test the generalization performance of the established model, a simulation test was done by taking examples of 4 major types of fault diagnosis of the main transformer in a certain substation. The simulation result shows that the fault diagnosis model based on discrete Hopfield neural network has high accuracy, fast speed and good generalization performance, and the practicability and validity of the proposed method are also verified.

To deal with low accuracy problem of prediction of laboratory equipment demand, a new demand prediction model for laboratory equipment was established combining grey relation analysis and Elman (GRA-Elman) neural network.First, the correlation between various influence factors was calculated using grey relation analysis method. Then three factors with higher correlation were chosen as training data of Elman neural network. The original 6-12-1 Elman network model is reduced to a 3-6-1model. The algorithm was tested based on the actual demand of a certain equipment, and the prediction accuracies of GRA-Elman network model, single Elman network model and single BP network model were compared.The simulation results show that the training time of GRA-Elman network model is the shortest; the maximum relative error of its prediction is -0.67%, while those of single Elman network model and the single BP network model are -2.61% and -4.18% respectively. So the established GRA-Elman model has higher prediction precision and simpler structure.

Discrete Hopfield neural network(DHNN)with an associative memory function was used to diagnose gearbox faults of wind turbines. Five fault feature indexes in domains of both time and frequency are treated as evaluation factors. A DHNN model capable of diagnosing three types of gearbox faults of wind turbines was established using MATLAB toolbox. And the model was applied to the diagnosis of the real data collected from a wind farm in northern China to test its generalization ability. The simulation results show that DHNN has high diagnosis accuracy ,fast convergence speed as well as high practicality.

To accurately analyze the sinusoidal steady‐state circuit ,a simulation model of a typical sinusoidal steady‐state circuit is established by using M language and Simulink toolbox in this paper .The establishment processes of a circuit system’s mathematical model and simulation model are described and simulated in the environment of MATLAB/Simu‐link .The simulation results show that it is simple to establish a simulation model of a circuit system using M language and Simulink toolbox .The simulation results are the same and they correspond with the theoretical analysis .The two methods are applicable in the establishment of simulation models of middle and small scale circuit systems ,but Simulink toolbox is more effective in the modeling and simulation of large scale circuit systems .

为了研究自组织特征映射神经网络在对于二维向量进行模式分类时,网络结构的最优化问题,深入研究了SOFM神经网络的结构和算法,说明了SOFM网络的建立方法.以二维向量的模式分类为例,利用所建立的SOFM网络模型对输入的二维向量模式进行分类,研究了输出层节点形状和拓扑结构对分类结果的影响,测试了在不同的训练步数条件下,SOFM模型的权值向量的调整过程和分类效果.仿真结果表明:当网络的输出节点以二维平面形式输出时,长和宽不相等的矩形图的分类性能明显优于正方形图的分类性能,并且在输出节点形式相同的情况下,六边型拓扑结构分类精度明显优于栅格型拓扑结构的SOFM神经网络.