Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

Distributed Fault-Tolerant Consensus Tracking of Multi-Agent Systems Under Cyber-Attacks

This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks. First, a decentralized unknown input observer is established to estimate relative states and actuator faults. Second, the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers. Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time, attack frequency, and attack activation rate technique, respectively. Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.      

基于观测器的多智能体系统一致性控制与故障检测

针对一类受到未知干扰的非线性多智能体系统,提出了一种鲁棒一致性控制与故障检测算法.首先,针对每个智能体系统设计了一个未知输入非线性观测器.然后,基于观测器的状态估计信息,设计了鲁棒一致性控制协议.控制协议保证了给定的干扰抑制性能指标.接着,考虑智能体出现故障的情形,采用自适应阈值法,提出了一种分布式故障检测算法.最后,以多个直流电机驱动的单摆系统为例进行了仿真实验,仿真结果表明了一致性控制与故障检测算法的有效性.     

Finite-time consensus of time-varying nonlinear multi-agent systems

This paper investigates the problem of leader–follower finite-time consensus for a class of time-varying nonlinear multi-agent systems. The dynamics of each agent is assumed to be represented by a strict feedback nonlinear system, where nonlinearities satisfy Lipschitz growth conditions with time-varying gains. The main design procedure is outlined as follows. First, it is shown that the leader–follower consensus problem is equivalent to a conventional control problem of multi-variable high-dimension systems. Second, by introducing a state transformation, the control problem is converted into the construction problem of two dynamic equations. Third, based on the Lyapunov stability theorem, the global finite-time stability of the closed-loop control system is proved, and the finite-time consensus of the concerned multi-agent systems is thus guaranteed. An example is given to verify the effectiveness of the proposed consensus protocol algorithm.     

非仿射非线性多智能体系统迭代学习一致跟踪

为了解决非仿射非线性多智能体系统在给定时间区间上一致性完全跟踪问题,基于迭代学习控制方法设计一种分布式一致性跟踪控制算法.首先,由引入的虚拟领导者与所有跟随者组成多智能体系统的通信拓扑,其中虚拟领导者的作用是提供期望轨迹.然后,在只有部分跟随者能够获得领导者信息的条件下,利用每个跟随者及其邻居的跟踪误差构造每个跟随者的迭代学习一致性跟踪控制器.同时采用中值定理将非仿射非线性多智能体系统转化仿射形式,并基于压缩映射方法证明所提算法的收敛性,给出算法的收敛条件.理论分析表明,在智能体的非线性函数未知情况下,利用所提算法可以使非仿射非线性多智能体系统在给定时间区间上随迭代次数增加逐次实现一致性完全跟踪.最后,通过仿真算例进一步验证所提算法的有效性.     

Distributed data-driven consensus control ofmulti-agent systems under switched uncertainties

Cooperative control of multi-agent systems (MASs), particularly consensus control, has gained significant attention in the last two decades, thanks to the rapid and sustained development of distributed and networked systems. In this paper, we present some new results focused on consensus control of a set of unknown linear MASs (whose system matrices are unknown) under unknown switched uncertainties, with an emphasis on distributed data-driven controllers. The proposed controller is end-toend, designed by solving two data-based semi-definite programs (SDPs), which adjust to the changes of the uncertainty modes. Our approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is small. We illustrate the effectiveness of our proposed method through a numerical example.     

Distributed fuzzy adaptive event-triggered finite-time consensus tracking control for uncertain nonlinear multi-agent systems with asymmetric output constraint

This article investigates the consensus problem for uncertain nonlinear multi-agent systems (MASs) with asymmetric output constraint. Different from BLF-based constraint consensus tracking control, a novel approach based on nonlinear state-dependent function is proposed to solve the asymmetric output constraint, which need not convert output constraint into tracking error bound. First-order sliding mode differentiator is incorporated into each step of backstepping control design to reduce computation burden. Further, in combination of proposed event-triggered mechanism based on time-varying threshold, a distributed fuzzy adaptive event-triggered finite-time consensus method is developed. It can ensure that the consensus tracking error tends to a small neighbor in a finite time and the asymmetric output constraint of each subsystem is not violated. Two simulations are given to demonstrate the effectiveness of control method.     

未知异构非线性多智能体系统的无模型自适应编队控制

研究一类未知异构非线性多智能体的编队控制问题.首先,利用全格式动态线性化(full form dynamic linearization,FFDL)方法将未知非线性智能体转化为含有时变参数的数据模型,并给出时变参数的估计方法;然后,基于该数据模型设计一种分布式无模型自适应多智能体编队控制方案;最后,为验证所提出的无模型自适应编队控制方案的有效性,利用3台NAO机器人开发基于Python的多智能体编队控制实验平台.实验比较结果表明,通过所提出的控制方案可使3台机器人仅利用局部信息就能有效完成编队控制任务,控制性能优于基于PID的编队控制方法.     

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.     

Global consensus of second-order unknown multi-agent systems: A distributed fuzzy iterative learning scheme

This article focuses on global fuzzy consensus control of unknown second-order nonlinear multi-agent systems based on adaptive iterative learning scheme. In order to achieve global consensus, a replacement idea is introduced, where fuzzy systems are used as feedforward compensators to model unknown nonlinear dynamics relying on tracking signals. Considering that the network communication is distributed, a kind of hybrid control protocol is designed to avoid the complete dependence on the tracking signals. In addition, considering the complexity of the external environment, this article extends the above distributed protocol to the case of unknown control directions to study global consensus. Finally, the feasibility of the proposed protocols is verified by Matlab numerical simulations.     

Practical time-varying formation tracking for high-order nonlinear multi-agent systems based on the distributed extended state observer

Practical time-varying formation tracking analysis and design problems for high-order nonlinear multi-agent systems are investigated, where the time-varying formation tracking error is controlled within an arbitrarily small bound. The states of followers form a predefined time-varying formation while tracking the state of the leader with unknown control input. Besides, the dynamics of each agent has heterogeneous nonlinearity and disturbance. First, a distributed extended state observer is constructed to estimate the follower's nonlinearity and disturbance, and the leader's unknown control input simultaneously. A protocol based on the distributed extended state observer is proposed. Second, sufficient conditions for the multi-agent systems to achieve the practical time-varying formation tracking under the protocol are presented by using the Lyapunov stability theory. Third, an approach is derived to design the proposed protocol by solving a linear matrix inequality. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.     

Dynamic event-triggered bipartite consensus for uncertain high-order nonlinear multi-agent systems

In this paper, the bipartite consensus problem is studied for a class of uncertain high-order nonlinear multi-agent systems. A signed digraph is presented to describe the collaborative and competitive interactions among agents. For each agent with lower triangular structure, a time-varying gain compensator is first designed by relative output information of neighboring agents. Subsequently, a distributed controller with dynamic event-triggered mechanism is proposed to drive the bipartite consensus error to zero. It is worth noting that an internal dynamic variable is introduced in triggering function, which plays an essential role in excluding the Zeno behavior and reducing energy consumption. Furthermore, the dynamic event-triggered control protocol is developed for upper triangular multi-agent systems to realize the bipartite consensus without Zeno behavior. Finally, simulation examples are provided to illustrate the effectiveness of the presented results.     

基于滑模的二阶多智能体快速抗扰一致性

针对带有外部未知扰动的二阶多智能体系统的领导—跟随有限时间一致性问题，本文设计出一种带有时变增益的有限时间干扰观测器，用以实现对每个跟随智能体中未知扰动的快速估计，在此基础上，本文结合超螺旋积分滑模控制方法并利用邻居智能体的位置和速度信息设计一种快速抗扰一致性协议，该协议能够保证存在非线性动态的多智能体系统有限时间一致性控制并能抑制抖振现象.同时，利用李亚普诺夫函数进行了稳定性的证明.最后，通过Matlab数值仿真进一步验证了所提出协议的可行性.     

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.     

Consensus of single integrator multi-agent systems with directed topology and communication delays

In this paper, a distributed control scheme has been developed for consensus of single integrator multi-agent systems with directed fixed communication topology for arbitrarily large constant, time-varying or distributed communication delays. It is proved that the closed loop control system can reach consensus with an exponential convergence rate if and only if the topology is quasi-strongly connected. Simulation results are also provided to demonstrate the effectiveness of the proposed controller.     

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.     

Adaptive Finite-time Consensus for Second-order Nonlinear Multi-agent Systems with Input Quantization

In this paper, the adaptive finite-time consensus (FTC) control problem of second-order nonlinear multi-agent systems (MASs) with input quantization and external disturbances is studied. With the help of finite time control technology, a novel distributed adaptive control protocol is constructed to achieve FTC performance for second-order nonlinear MASs by using the recursive method. The control input is quantized through a hysteresis quantizer, which reduces the communication rate of arbitrary two agents. The unknown functions are approximated by adopting the radial basis function neural networks. Under the consensus protocols and adaptive laws, it can be proved that velocity errors of arbitrary two agents reach a small region of zero in finite time as well as position errors. Finally, the effectiveness of the proposed method is illustrated via a simulation example.

     

A Continuous Leader-following Consensus Control Strategy for a Class of Uncertain Multi-agent Systems

In this paper, we study the robust leader-following consensus problem for a class of multi-agent systems with unknown nonlinear dynamics and unknown but bounded disturbances. The control input of the leader agent is nonzero and not available to any follower agent. We first consider a class of high order chain integrator-type multi-agent systems. By employing the robust integral of the sign of the error technique, a continuous distributed control law is constructed using local information obtained from neighboring agents. Using Lyapunov analysis theory, we show that under a connected undirected information communication topology, the proposed protocol achieves semiglobal leader-following consensus. We then extend the approach to a class of more general uncertain multiagent systems. A numerical example is given to verify our proposed protocol.      

Error-transformation-based consensus algorithms of multi-agent systems: connectivity-preserving approach

This paper investigates distributed connectivity-preserving consensus problems of networked multi-agent systems with limited communication ranges. Compared with existing literature, a main contribution of this paper is to present a new nonlinear transformation approach of consensus errors for preserving the initial interaction patterns of multi-agent systems. Both the consensus and the connectivity preservation can be achieved by using one transformed error function. Based on the proposed nonlinear error transformation, we derive distributed connectivity-preserving consensus algorithms for single-integrator dynamics, double-integrator dynamics and second-order nonlinear systems. The asymptotic stability of consensus errors and the connectivity preservation among agents are established through Lyapunov stability analysis.     

基于神经网络的电力系统暂态稳定分布式自适应控制

针对电力系统中普遍存在的系统非线性和参数不确定性等问题,提出一种基于径向基函数神经网络(RBFNN)的分布式自适应控制器,以提高多机电力系统的暂态稳定性.利用基于RBFNN的方法对系统中的未知非线性项和外部扰动进行补偿,设计相应的自适应参数估计方法,逼近未知非线性项的理想权值矩阵.该策略基于多智能体框架,分布式控制器通过通信网络接收测量装置测量的实时数据,并控制储能装置动作,使受到扰动后各发电机能够迅速实现频率同步.利用李雅普诺夫稳定性理论,证明所提出的分布式控制方法的收敛性.最后,通过仿真研究验证所提出的分布式控制方法的有效性.