Nonfragile containment control of nonlinear multi-agent systems via a disturbance observer-based approach

In this article, we consider the nonfragile containment control problem of nonlinear multi-agent systems (MASs) with exogenous disturbance where the communication links among agents under consideration is directed. Firstly, based on relative output measurements between the agent and its neighbors, a disturbance observer-based control protocol is proposed to solve the containment control problem of MASs with inherent nonlinear dynamics and exogenous disturbances. Secondly, because of the additional tuning of parameters in the real control systems, uncertainties in the designing of observer and controller gains always occur, and as a result, an output feedback controller with disturbance rejection is conceived and the containment control problem of nonlinear MASs with nonfragility is thoroughly investigated. Then, depending on matrix transformation and inequality technique, sufficient conditions of the designed controller gains exist, which is derived from the asymptotic stability analysis problem of some containment error dynamics of MASs. Finally, two simulation examples are exploited to illustrate the effectiveness of the proposed techniques.     

Distributed event-triggered optimal bipartite consensus control for multiagent systems with input delay via reinforcement learning method

In this article, an optimal bipartite consensus control (OBCC) scheme is proposed for heterogeneous multiagent systems (MASs) with input delay by reinforcement learning (RL) algorithm. A directed signed graph is established to construct MASs with competitive and cooperative relationships, and model reduction method is developed to tackle input delay problem. Then, based on the Hamilton–Jacobi–Bellman (HJB) equation, policy iteration method is utilized to design the bipartite consensus controller, which consists of value function and optimal controller. Further, a distributed event-triggered function is proposed to increase control efficiency, which only requires information from its own agent and neighboring agents. Based on the input-to-state stability (ISS) function and Lyapunov function, sufficient conditions for the stability of MASs can be derived. Apart from that, RL algorithm is employed to solve the event-triggered OBCC problem in MASs, where critic neural networks (NNs) and actor NNs estimate value function and control policy, respectively. Finally, simulation results are given to validate the feasibility and efficiency of the proposed algorithm.     

Robust output regulation for containment control of heterogeneous discrete-time nonlinear multi-agent systems

ABSTRACT

In existing researches on containment control of heterogeneous multi-agent systems (MASs), the solution is usually dependent on the solvability of regulator equations. However, the closed-form solution of many nonlinear regulator equations of systems is rarely obtained. Towards this end, in this paper the containment control problem of heterogeneous discrete-time nonlinear MASs subject to parameter uncertainties is considered, and the power series approach is adopted to solve complex regulator equations by decomposing them into a series of solvable linear equations. Then, a distributed robust control law based on internal model principle is presented by utilising the solution of the linear equations. Theoretical analysis shows that under certain assumptions asymptotic containment control is achieved for the heterogeneous discrete-time nonlinear MASs with sufficiently small parameter perturbations. Finally, a numerical simulation is implemented to verify the proposed control law.     

Cooperative semi-global robust output regulation for a class of nonlinear uncertain multi-agent systems

In this paper, we study the cooperative semi-global robust output regulation problem for a class of minimum phase nonlinear uncertain multi-agent systems. This problem is a generalization of the leader-following tracking problem in the sense that it further addresses such issues as disturbance rejection, robustness with respect to parameter uncertainties. To solve this problem, we first introduce a type of distributed internal model that converts the cooperative semi-global robust output regulation problem into a cooperative semi-global robust stabilization problem of the so-called augmented system. We then solve the semi-global stabilization problem via distributed dynamic output control law by utilizing and combining a block semi-global backstepping technique, a simultaneous high gain feedback control technique, and a distributed high gain observer technique.     

\begin{document}$H_\infty$\end{document} Consensus Control of Discrete-Time Multi-Agent Systems Under Network Imperfections and External Disturbance

This paper presents a distributed control protocol for consensus control of multi-agent systems (MASs) under external disturbances and network imperfections, including communication delay and random packet dropout. To comply with the discrete nature of networked systems, in contrast to most of the existing work for MASs under network imperfections, the agents are modeled by discrete-time dynamics. The communication network is considered to be undirected, its delay is considered to be time-varying but bounded, and its packet dropout is modeled by a Bernoulli distributed white sequence. Sufficient conditions in terms of linear matrix inequalities (LMIs) for asymptotic mean-square consensus stability are derived under network imperfections without considering external disturbances. A desired disturbance attenuation level in the presence of both external disturbances and network imperfections is also provided. A simulation example is given to verify the effectiveness of the proposed approach in coping with network imperfection and disturbances.      

基于梯度估计的多智能体系统有限时间分布式优化

现有多智能体系统分布式优化算法大多具有渐近收敛速度,且要求系统的网络拓扑图为无向图或有向平衡图,在实际应用中具有一定的保守性.本文研究了具有强连通拓扑的多智能体系统有限时间分布式优化问题.首先,基于非光滑分析和Lyapunov稳定性理论设计了一个有限时间分布式梯度估计器.然后,基于该梯度估计器提出了一种适用于强连通有向图的有限时间分布式优化算法,实现了多智能体系统中智能体的状态在有限时间内一致收敛到全局最优状态值.与现有的有限时间分布式优化算法相比,新提出的有限时间优化算法适用于具有强连通拓扑的多智能体系统,放宽了系统对网络拓扑结构的要求.此外,本文基于Nussbaum函数方法对上述优化算法进行了拓展解决了含有未知高频增益符号的多智能体系统分布式优化问题.最后,通过仿真实例对提出的分布式优化算法的有效性进行了验证.     

通信延时和数据丢包下事件驱动的多智能体系统一致性研究

本文研究受网络通信延时和数据随机丢包的多智能系统一致性问题, 探索事件驱动的分布式协同控制策略. 首先针对两类普遍应用的事件触发器, 提出了一个可用于选择触发策略的触发频率比较方法. 然后提出了分布式协同控制律以保证系统的渐近一致性, 并给出了相应的时滞依赖Markov切换控制器设计新方法. 本文所提的控制策略不仅保证系统一致性目标, 而且能显著减少通信数据传输量并降低控制器计算负担. 最后,通过仿真算例验证了所提方法的有效性。     

多智能体系统输入约束下的一致性与轨迹规划研究

针对多智能体系统提出了一种分布式预测控制方法. 首先, 研究了有输入约束下的一致性问题. 其次, 对环境中有障碍物的多智能体轨迹规划进行了研究, 其中只有当障碍物进入智能体有限感知区域内时, 障碍物状态信息才能被获取. 基于预测控制方法, 设计了一种分布式控制算法来解决上面两个问题. 构造一个与每个智能体动力学相交互的代价函数, 设计相应最优控制问题, 从而实现优化控制算法. 智能体间交互信息是其邻居在上一时刻的最优控制状态. 系统稳定性可以通过构造代价函数中的一个终点状态控制器与最优控制问题中的一个终点状态区域来保证. 仿真研究表明所提方法的有效性.     

Distributed convex optimisation with event-triggered communication in networked systems

This paper studies the distributed convex optimisation problem over directed networks. Motivated by practical considerations, we propose a novel distributed zero-gradient-sum optimisation algorithm with event-triggered communication. Therefore, communication and control updates just occur at discrete instants when some predefined condition satisfies. Thus, compared with the time-driven distributed optimisation algorithms, the proposed algorithm has the advantages of less energy consumption and less communication cost. Based on Lyapunov approaches, we show that the proposed algorithm makes the system states asymptotically converge to the solution of the problem exponentially fast and the Zeno behaviour is excluded. Finally, simulation example is given to illustrate the effectiveness of the proposed algorithm.     

Internal model approach to cooperative robust output regulation for linear uncertain time‐delay multiagent systems

In this paper, we study the cooperative robust output regulation problem for linear uncertain multiagent systems with both communication delay and input delay by the distributed internal model approach. The problem includes the leader‐following consensus problem of linear multiagent systems with time delay as a special case. We first generalize the internal model design method to systems with both communication delay and input delay. Then, under a set of standard assumptions, we have obtained the solution to the problem via both the state feedback control law and the output feedback control law. In contrast to the existing results, our results apply to general linear uncertain multiagent systems, accommodate a large class of leader signals, and achieve asymptotic tracking and disturbance rejection at the same time.     

Encryption–decryption-based event-triggered consensus control for nonlinear MASs under DoS attacks

In this article, the consensus tracking problem is discussed for a class of discrete-time nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks. The individual agents interact with each other via communication network whose topology is assumed to be time-varying and strongly connected. Two techniques are employed to deal with the network-induced complexities. On one hand, an event-triggering scheme is adopted to regulate the data transmission among agents with the purpose of making full utilization of the limited communication resources; and on the other hand, an encryption–decryption mechanism is designed with the aim to provide compensation, thereby mitigating the DoS attack effects. It is the objective of the addressed problem to develop a distributed model-free adaptive control law to enforce the MASs achieve desirable consensus performance. By using a specific projection algorithm in combination with a dynamic linearization method, the desired control protocol is formulated explicitly, whose effectiveness and applicability are demonstrated via an illustrative numerical example.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

Distributed MPC of polytopic uncertain systems: handling quantised communication and packet dropouts

In this paper, we study the distributed model predictive control (MPC) of polytopic uncertain systems with quantised communication and packet dropouts. The model of the whole plant is divided into a certain number of incomplete subsystems. Due to the nature of the distributed control structure, there is generally a lack of information about the state of the overall system. Each subsystem shares its information with neighbour subsystems via reliable connection. Distributed MPC controllers are designed for each subsystem by solving the linear matrix inequalities optimisation problem. The distributed state feedback laws are quantised and transmitted via communication network. An iterative algorithm is presented to make coordination among distributed state feedback laws. The communication is assumed to be affected by random packet dropouts in a representation of Bernoulli distributed white sequences with known conditional probabilities. A case study is carried out to demonstrate the effectiveness of the proposed distributed MPC technique.     

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.     

A centre–free approach for resource allocation with lower bounds

Since complexity and scale of systems are continuously increasing, there is a growing interest in developing distributed algorithms that are capable to address information constraints, specially for solving optimisation and decision-making problems. In this paper, we propose a novel method to solve distributed resource allocation problems that include lower bound constraints. The optimisation process is carried out by a set of agents that use a communication network to coordinate their decisions. Convergence and optimality of the method are guaranteed under some mild assumptions related to the convexity of the problem and the connectivity of the underlying graph. Finally, we compare our approach with other techniques reported in the literature, and we present some engineering applications.     

含时延约束的多智能体系统二分一致性

针对多智能体系统中信息交互存在通信时延这一约束,在无向符号图拓扑结构下分别研究了含固定时延和时变时延的一阶多智能体系统二分一致性问题。通过设计相应的控制协议,使得该系统收敛到两个模值相同但符号不同的状态。在稳定性分析中,利用广义Nyquist准则的方法,得到含固定时延多智能体系统实现二分一致性的充分条件;对含时变时延系统构造包含三重积分项的Lyapunov函数,利用积分不等式和线性矩阵不等式理论,并结合自由矩阵的方法得到含时变时延多智能体系统实现二分一致性的充分条件。最后,数值仿真验证了所得结论的有效性和正确性。     

Neural network-based event-triggered cluster quasi-consensus for unknown multiagent systems with directed topology

This paper studies cluster quasi-consensus problem for a class of unknown nonlinear multiagent systems (MASs) with directed communication topology. First, a distributed continuous neural network (NN)-based adaptive protocol is presented for solving this problem by introducing reference model to each agent. Then, taking limited communication resource and energy consumption into account, a distributed event-triggered cluster quasi-consensus protocol is proposed. Different from the existing results, two event-triggered mechanisms are constructed in the proposed event-triggered protocol to reduce communication load and control update frequency as possible. The sufficient conditions that guarantee cluster quasi-consensus under the both proposed protocols are obtained, respectively. Zeno behavior is proved to be excluded. Finally, simulation results verify the effectiveness of the proposed protocols.     

Distributed resource allocation of second‐order multiagent systems with exogenous disturbances

In this paper, we study the resource allocation problem of second‐order multiagent systems with exogenous disturbances, and the communication networks are weight‐balanced digraphs. Different from the well‐studied resource allocation problems, our problem involves the disturbed second‐order dynamics of agents. In order to achieve the optimal allocation, we propose a distributed algorithm based on gradient descent and internal model approach. Furthermore, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Moreover, we prove that the agents in the network can achieve the exact optimal allocation even in the presence of external disturbances. Finally, we provide two examples to illustrate our result.     

切换拓扑下非线性多智能体系统自适应神经网络一致性

本文研究一类具有未知控制系数的非线性多智能体系统自适应神经网络分布式控制策略.首先,针对切换拓扑下具有未知控制系数的非线性多智能体系统一致性问题,提出一类自适应神经网络一致性控制算法.其中,采用神经网络函数逼近方法解决系统中的不确定性问题,并设计一项自适应光滑项处理有界扰动和神经网络函数逼近误差.随后,证明了切换拓扑下具有未知控制系数的非线性多智能体系统的一致性,并保证了闭环系统的有界性.此外,本文把相关的一致性算法扩展到了一般有向图含有一个有向生成树的情形.最后,通过仿真实例验证了本文所提算法的有效性.     

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.