Prescribed performance global stable adaptive neural dynamic surface consensus tracking control of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics

This paper focuses on the leader-following consensus control problem of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics. A leader-following consensus scheme is presented for stochastic multi-agent systems directions under directed graphs, which can achieve predefined synchronisation error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded tracking stability of multi-agent systems recently. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronise with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and all the follower agents can keep consensus with the leader. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.     

Event-triggered based bearing-only formation control for nonlinear multi-agent with unknown disturbance

This article delves into the bearing-only formation tracking control problem of nonlinear multi-agent systems with unknown disturbances and unmodeled dynamics, wherein the distributed control solely utilizes the relative bearing information of its neighbors. Firstly, a disturbance observer combined with a neural network is proposed to eliminate the impact of unknown disturbances and unmodeled dynamics. Additionally, a pioneering event-triggered based backstepping control approach is put forth for the formation tracking control problem of nonlinear multi-agent systems, which economizes on communication bandwidth and computing resources by decreasing the update frequency of the controller. Finally, using the Lyapunov method, it is demonstrated that all signals of the control system are bounded, and the convergence errors are confined to a small neighborhood of the origin while excluding the “Zeno behavior” phenomenon through rigorous proof.     

Event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control directions and actuator saturation

This article addresses the event-triggered adaptive consensus control of nonlinear multi-agent systems with unknown control direction and actuator saturation. A new robust adaptive control algorithm based on an event-triggered mechanism is designed. The smooth Lipschitz function approximates the saturated nonlinear function, while the Nussbaum function handles unknown control directions and residual terms. The event-triggered mechanism is designed to determine the time of communication, significantly reducing the communication burden. An additional estimator is utilized to deal with unknown parameters involved in neighbor dynamics and prevent information exchange to consistency errors between connected subsystems. The results show that all the signals of the closed-loop system are uniformly bounded, and the consensus tracking error converges to a bounded set. Meanwhile, Zeno's behavior is eliminated. Simulation results confirm the superiority of the proposed method.     

Mean square consensus of stochastic multi-agent systems with nonlinear dynamics by distributed event-triggered strategy

This paper is concerned with the problem of mean square consensus for nonlinear multi-agent systems with state-dependent noise perturbations. A distributed event-triggered mechanism which can be used to reduce the number of controller updates and communication load is developed to apply in stochastic dynamical systems. By combining the tools of graph theory and stochastic analysis, the sufficient conditions are given to ensure that mean square consensus in the multi-agent systems can be achieved exponentially. The convergence rate is also analytically derived. Moreover, the feasibility of the proposed distributed event-triggered strategy is further verified by exclusion of Zeno behaviour. Finally, some numerical examples are given to demonstrate the effectiveness of the obtained results.     

基于间歇事件触发控制二阶多智能体网络的一致性*

针对二阶多智能体网络在无向连通拓扑结构下的间歇控制一致性问题,引入了合适的事件触发控制算法,该算法能够有效的减少控制器的更新次数。同时,模型中的每个智能体具有非线性项,使得多智能体网络更为实际。采用Lyapunov-Krasovskii泛函方法,对网络进行了一致性分析,给出了事件触发控制算法下二阶多智能体网络趋于一致的充分条件,得到了网络通讯间隔的要求,并排除了Zeno现象。最后,数值仿真结果进一步验证了理论分析的正确性。     

自适应事件触发的马尔科夫跳变多智能体系统一致性

针对非线性马尔科夫跳变多智能体系统在有向固定拓扑下的领导跟随一致性问题,为减少智能体间不必要的通信传输,节约网络资源,保证系统性能,提出一种自适应事件触发控制策略.首先,将每一个智能体均视为马尔科夫跳变系统,且马尔科夫链的转移概率部分未知;通过简单的模型转换建立误差系统,将多智能体系统一致性问题转化为误差系统的稳定性问题;在此基础上,构造合适的Lyapunov-Krasovskii泛函并利用Jensen不等式和线性矩阵不等式等技术给出使多智能体系统达到领导跟随一致性的充分条件及控制器设计方法;通过求解线性矩阵不等式可以得到多智能体系统一致性控制器增益矩阵和事件触发参数矩阵;最后,通过数值仿真验证所提出方法的有效性.     

随机非线性系统基于事件触发机制的自适应神经网络控制

针对一类具有严格反馈结构且控制方向未知的随机非线性系统，提出了基于事件触发机制的自适应神经网络（Adaptive neural network，ANN）输出反馈控制方法.利用径向基神经网络逼近系统中未知的非线性函数.通过引入Nussbaum增益函数并设计滤波器，解决了系统控制方向未知的问题.通过设计具有相对阈值的事件触发机制，保证了闭环随机非线性系统的有界性.最后给出数值仿真例子验证所提控制方法的有效性.     

Generalised exponential consensus of the fractional-order nonlinear multi-agent systems via event-triggered control

In this paper, the leader-following consensus problem of the fractional-order nonlinear multi-agent systems via event-triggered control is considered. An effective event-triggered controller is designed and then generalised exponential consensus of the controlled multi-agent systems is studied in the sense of Mittag-Leffler stability of fractional-order systems. The event-triggering function design is dependent on the parameter of the system structure and the minimum inter-event interval can be flexibly adjusted with different fractional-order α. With the event-triggered control scheme, the consensus condition is obtained and the convergence rate of the system is estimated. Numerical simulation indicates the effectiveness of the theoretical results.     

Observer-based Finite-time Control of Stochastic Non-strict-feedback Nonlinear Systems

This paper investigates the observer-based adaptive finite-time neural control issue of stochastic non-strict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.

     

Event-based containment control for multi-agent systems with packet dropouts

This paper is concerned with the event-triggered containment control for stochastic multi-agent systems subject to packet dropouts. The adopted event-triggered protocol is with absolutely triggered conditions catering for the requirement of real-time to an extreme. In light of such a protocol, a novel definition of containment control in mean-square sense, named as χ-containment, is proposed to better describe the tracking dynamics of followers. Based on relative measurement outputs, the purpose of this paper is to design an output-feedback controller such that all followers converge into the convex hull spanned by leaders. First, with the help of the property of the Laplacian matrix, the containment control problem is transformed to an easily setting step by step. Then, sufficient conditions with the form of matrix inequalities are derived to guarantee the desired χ-containment which depends on the initial values and the event-triggered thresholds. By introducing a free matrix combined with an orthogonal basis of the null space of control matrix, the controller gain can be obtained by solving a set of linear matrix inequalities. Finally, a simulation example is given to verify the effectiveness of the designed control protocol.     

Event-triggered adaptive tracking control for nonlinear systems with input saturation and unknown control directions

This article is concerned with event-triggered adaptive tracking control design of strict-feedback nonlinear systems, which are subject to input saturation and unknown control directions. In the design procedure, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. The Nussbaum gain technique is employed to address the issue of the unknown control directions. A predetermined time convergent performance function and a nonlinear mapping technique are introduced to guarantee that the tracking error can converge in the predetermined time with a fast convergence rate and a high accuracy. Then the event-triggered adaptive prescribed performance tracking control strategy is proposed, which not only ensures the boundedness of all the closed-loop signals and the convergence of tracking error but also reduces the communication burden from the controller to the actuator. At last, the simulation study further tests the availability of the proposed control strategy.     

多智能体系统的事件驱动控制

近年来事件驱动控制发展迅速,并引起了多智能体系统领域研究者的极大关注.本文对基于事件驱动控制的多智能体系统的研究现状进行综述.从智能体动力学角度,分别对这个领域的一些代表性成果和研究方法进行了归纳总结.进一步,论述了边事件驱动控制策略下的多智能体系统的研究成果.随后,利用一类新型事件驱动控制来探讨多智能体系统的一致性问题.最后,给出了尚未解决的问题和未来值得关注的研究方向.     

Distributed Adaptive Dynamic Surface Control for Synchronization of Uncertain Nonlinear Multi-agent Systems

In this paper a distributed adaptive dynamic surface controller is proposed for multi-agent systems under fixed directed graph topologies. The agents have uncertain nonlinear dynamics and are influenced by bounded unknown disturbances. The controller should synchronize the states of all agents with the corresponding states of the nonautonomous leader. It is proved that, with the proposed controller, the synchronization error remains bounded; and the bounds can be arbitrarily decreased by increasing the controller gains. The control rules are designed such that each agent only requires the state information of its neighbors, rendering a distributed control. The effectiveness of the proposed method is demonstrated through two simulation examples.     

Event-triggered Control of Positive Systems With State Saturation Using Linear Programming

This paper proposes the event-triggered control of positive systems with state saturation both in discrete-time and continuous-time cases. A 1-norm based event-triggered mechanism is established for positive systems. Under the event-triggered mechanism, the error term between actual and sample states is transformed into interval uncertain form. Together with the properties of saturation, the systems with state saturation are transformed into interval uncertain systems and the corresponding lower and upper bounds of the system matrices are obtained, respectively. Using a linear co-positive Lyapunov function, the event-triggered controller and the auxiliary gain matrix of the domain of attraction are designed in terms of linear programming, respectively. Then, the systems with state and input saturation are also described via interval uncertain systems. An event-triggered controller is designed and thus the closed-loop systems are positive and stable under the designed controller. Furthermore, the presented event-triggered control approach is extended to the continuous-time case. Compared to existing control approaches, the event-triggered control can reduce energy consumption and increase the practicability. Finally, several numerical examples are given to illustrate the effectiveness of the proposed design.

     

状态时延和全状态约束下的多智能体系统自适应事件触发控制

研究有向通信图下非线性多智能体系统的一致控制问题.首先,通过引入性能函数,使输出误差满足预定性能;其次,采用障碍Lyapunov函数,保证所有状态满足约束条件,结合李雅普诺夫-克拉索夫斯基(Lyapunov-Krasovskii,LK)泛函和杨氏不等式消除状态时延的影响,利用径向基函数神经网络(radial basis...     

Distributed Event-triggered Containment Control for Discrete-time Multi-agent Systems

This paper investigates the event-triggered containment control for discrete-time multi-agent systems. Event-triggered control strategies are employed in order to reduce the number of controller actuation updates for multi-agent systems with limited resources. It is assumed that each follower in the system updates its state only at some instants which are determined by the proposed event-triggered condition. Both centralized and decentralized event-triggered strategies are proposed to solve the containment control problem. Convergence analysis is given with the help of matrix theory and Lyapunov method, and it is showed that the proposed strategy does not exhibit Zeno behaviors. Numerical simulations are given to illustrate the effectiveness of the theoretical results.     

Distributed event-triggered consensus control for multiple Lur'e nonlinear systems under directed graph

This article addresses the distributed consensus problem of multi-agent systems with Lur'e nonlinear dynamics under directed graphs based on event-triggered strategy. With the state-dependent event-triggered thresholds, a novel distributed event-triggered consensus controller is designed, under which the consensus can be achieved with zero final consensus error. The proposed strategy has several distinguishing features, including the fact that individual agent does not require continuous, or even periodic, communication with their neighbours to update the controller or monitor the triggering condition, and all the required parameters can be locally determined by the agent. Furthermore, we also prove that there is no Zeno behaviour existed. Finally, the simulation example about Chua’s circuit is given to illustrate the theoretical analysis.     

Edge event-triggered control for multi-agent systems under directed communication topologies

The edge event-triggered consensus control for multi-agent systems with single- or double-integrator dynamics under directed communication topologies is investigated in this paper. With the edge event-triggered sampling strategies, a class of novel consensus protocols is proposed to reduce control update frequency. The main features of the proposed edge event-triggered control include three aspects: (i) measurement errors are defined based on directed information flow and thus they allow that the communication topology is directed, (ii) each individual controller updates its output only when the associated edge event occurs and the control actions are independent of other agents, and (iii) the proposed edge event-triggered control does not exhibit Zeno behaviour and it is shown that the inter-event intervals are lower bounded by a positive constant. In the consensus analysis, matrix transformation techniques are used to convert the high-dimensional system into a low-dimensional system, and some sufficient consensus conditions are derived for the multi-agent systems with single- or double-integrator dynamics. Finally, the effectiveness of the edge event-triggered sampling strategies is illustrated by simulations.     

Event-triggered Sliding Mode Fault-tolerant Consensus for a Class of Leader-follower Multi-agent Systems

This paper concerns with the robust sliding mode fault-tolerant consensus problem for a class of undertain second-order leader-follower multi-agent systems based on event-triggering strategies. First, a sliding mode fault-tolerant controller which uses the bound information of actuator failure rate and the event-triggering threshold is designed to ensure the robust consensus of the multi-agent systems, and the range of the sliding mode band is also shown. Second, by constructing an equivalent relationship, the upper bound of robust consensus error is also given. Third, the minimum event-triggered execution time for the second-order leader-follower multi-agent systems is calculated. Finally, the simulation results verify the effectiveness of the proposed event-triggered sliding mode fault-tolerant consensus algorithm.

     

含执行器故障的切换非线性系统的事件触发自适应模糊容错控制

本文研究在平均驻留时间约束下,一类含有执行器故障的切换非线性系统输出反馈自适应模糊事件触发容错控制问题.首先,建立了一个模态依赖的状态观测器估计不可测量状态.利用模糊逻辑系统来逼近未知项.其次,构建自适应模糊容错事件触发控制方案能够节省网络资源和数据传输.然后,通过构造多Lyapunov函数和平均驻留时间法,证明闭环系统所有状态半全局一致最终有界的同时排除了Zeno现象.最后,通过数值仿真验证该方法的有效性.