基于自适应分布式滤波观测器的多智能体系统编队控制

本文考虑了全局指令系统输出信息受到信道扰动情况下线性多智能体系统的编队控制问题.首先,基于协作式输出调节理论框架对线性多智能体系统的编队控制问题进行数学建模.其次,针对受到信道扰动的全局指令系统输出信息,提出了一类基于受扰输出的自适应分布式滤波观测器,在降低网络信息交换量的同时消除扰动的影响.最后,设计了输出反馈确定等价控制律,解决了线性多智能体系统的分布式编队控制问题.给出了数值仿真结果检验控制性能.     

非线性耦合动态互联系统分布式协作负载均衡控制

针对一类非线性耦合动态互联系统,提出一种分布式协作负载均衡控制方法.借助输入输出反馈线性化技术,构建系统输入输出之间的关系,将耦合互联系统的分布式负载均衡控制设计问题转化为一阶多速率积分器智能体系统的输出一致性跟踪问题.基于最近邻原则设计增益可调的分布式协作负载均衡控制律,在通信拓扑满足相应的连通条件下,通过子系统间的局部交互实现负载均衡的目标.仿真和实验结果表明了所提出控制方法的有效性和可行性.     

多速率分布式预测控制及其在热连轧活套系统中的应用

多智能体协调控制系统更适合采用分布式控制方式,但是处理智能体之间的耦合影响是分布式控制的一个难点.本文针对串联结构下的多智能体系统,提出一类多速率分布式预测控制策略,异步更新多智能体的控制律,能够充分考虑智能体之间的耦合影响,提高系统的稳定性,并给出了系统稳定的充分条件.最后,将多速率分布式控制算法应用到热连轧活套系统,仿真验证了方法的有效性和可行性.     

拓扑切换下IT2 T-S模糊非线性多智能体系统全局逆最优控制

针对连续非线性多智能体系统的全局最优协同控制问题,本文提出了模糊输出反馈和逆最优方法的分布式一致性最优控制律和相应的控制策略.首先,通过一种区间2型T-S (interval type 2 Takagi-Sugeno IT2 T-S)模糊模型将非线性系统等价转化为线性系统.其次,基于逆最优方法设计了全局最优协同控制律和相应的模糊输出反馈控制策略,智能体间仅仅通过局部通信,即可实现拓扑切换下非线性多智能体系统的二次性能全局最优控制,且系统的收敛速度大大提高.基于局部稳定性理论给出了全局逆最优控制的充要条件.最后,通过MATLAB算例验证所提方法的正确性和可行性.     

输出饱和多智能体系统的迭代学习趋同跟踪控制

针对带有输出饱和的多智能体系统有限时间趋同跟踪控制问题,提出了一种分布式迭代学习控制算法.首先假设多智能体系统具有固定拓扑结构,且仅有部分智能体可获取到期望轨迹信息.基于输出约束条件构造一致性跟踪误差,在此基础上设计了P型迭代学习控制率.然后采用压缩映射方法给出了一个算法收敛的充分条件,并在理论上证明了跟踪误差的收敛性.最后,将理论结果推广至具有随机切换拓扑结构的多智能体系统中.仿真结果验证了所提出算法的有效性.     

基于智能体的分布式多AGV控制系统设计与实现

提出一种基于智能体建模的多AGV分布式控制系统,详细讨论了单AGV智能体建模,协作情况下分布式多AGV体系结构以及系统中AGV内部各个模块的设计与实现.对提出的系统进行了现场实验,实验结果表明,本文设计的系统能够稳定运行并有效地完成任务.     

多智能体系统动态协调与分布式控制设计

多智能体系统的主要研究目的在于探索由个体之间的相互作用所产生的群体协调现象的内在机制和原理,而控制或反馈在多智能体协调运动中起着至关重要的作用．本文集中讨论了多智能体协调研究中的几个新兴的基本问题,包括输出调节、集合协调和覆盖．文中着重介绍了分布式估计和内模原理两种多智能体系统分布式输出调节方法及相关的研究进展：关于多智能体系统的目标集合协调,本文从集合聚集和集合优化两方面做了详尽论述：多智能体覆盖有多种分类方式,从覆盖对象的特征出发可将其划分为区域覆盖、边界覆盖和动态目标覆盖3种类型,并对它们的研究背景和最新成果予以介绍．另外文章还对多智能体系统协调控制的理论和应用研究进行了展望．     

具有复杂动力学的多智能体系统分布式优化综述

多智能体系统分布式优化由于其高效性、灵活性和可靠性等特点吸引了大量学者的关注,在多机器人协同控制、无线传感器网络、能源系统等领域具有广泛的应用前景.分布式优化的基本目标是利用智能体的个体目标函数梯度、自身及其邻居状态信息设计分布式控制协议,驱动所有智能体的状态或输出到全局目标函数的最优解,系统动力学是影响智能体状态演化的重要因素.鉴于此,在回顾现有连续时间分布式优化算法的基础上,根据系统动力学分类,尽可能全面地评述具有复杂动力学的多智能体系统分布式优化问题的最新研究进展,并对未来发展方向进行展望.     

协作机器人学的研究现状与发展

随着机器人向系统应用的方向发展，提出了由多机器 人构成的群体或社会的组织与控制问题．多机器人协作问题已成为机器人学研究领域的热点 之一．其中基于分布式人工智能中多智能体系统理论，研究多机器人协作问题正受到普遍关 注．本文对协作机器人学的研究现状进行了综述，并展望了其未来的发展．     

离散时变多智能体系统有限时间一致性迭代学习控制

针对一类离散时变多智能体系统,通过引入虚拟领导者产生期望轨迹的方法,将虚拟领导者和所有智能体组成固定的拓扑结构,在此基础上,提出一种离散时间迭代学习控制算法.该算法对多智能体系统中的每个智能体都设计一个控制器,各控制器都是利用上一次迭代时,该智能体与虚拟领导者之间的跟踪误差和该智能体与相邻智能体之间的跟踪误差,通过拓扑结构中通信权值的组合不断修正上一次的控制律,从而获得理想控制律.同时,基于范数理论严格证明所提出算法的收敛性,并给出算法在$\lambda$-范数意义下的收敛条件.该算法能够使离散时变多智能体的输出随着迭代次数的增加在有限时间区间内完全跟踪期望轨迹.理论分析和仿真结果都表明了所提出算法的有效性.     

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.     

Adaptive cooperative output regulation of nonlinear multiagent systems with arbitrarily large parametric uncertainties and an uncertain leader

This paper extends the result for cooperative output regulation problem for uncertain nonlinear multiagent systems in output feedback form in the sense that the exosystem generating leader's signal and disturbance is allowed to contain unknown parameter, and all parameters in the whole multiagent system can be arbitrarily large. Since only the information of itself and its neighbors is available, constructing a distributed control law is necessary for the asymptotic tracking of the uncertain leader's signal and the rejection of unknown external disturbances, which is also the main challenge here. A series of simulations are conducted to illustrate the efficiency and advantage of our designs together with the comparison of the design in the existing work.     

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.     

Formation control of singular multiagent systems with switching topologies

This paper is concerned with the time‐varying formation control problem for singular multiagent systems with switching topologies. First, in order to eliminate the pulse solution of singular systems and extend the formation function set, the distributed formation controller has been formulated based on the output information of the agents. Then, the explicit expression of formation position function is presented based on the impulse free and the equivalent transformation of singular multiagent systems. Next, the sufficient and necessary conditions of the feasibility of the formation function are provided. Moreover, the sufficient conditions of formation control of singular multiagent systems with switching topologies are presented and the algorithm is designed to solve the distributed controller. Finally, the validity of the proposed approaches is verified by numerical simulation in this paper.     

Cooperative robust output regulation problem for discrete‐time linear time‐delay multi‐agent systems

In this paper, we study the cooperative robust output regulation problem for discrete‐time linear multi‐agent systems with both communication and input delays by a distributed internal model approach. We first introduce the distributed internal model for discrete‐time multi‐agent systems with both communication and input delays. Then, we define the so‐called auxiliary system and auxiliary augmented system. Finally, we solve our problem by showing, under some standard assumptions, that if a distributed state feedback control or a distributed output feedback control solves the robust output regulation problem of the auxiliary system, then the same control law solves the cooperative robust output regulation problem of the original multi‐agent systems.     

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.     

Adaptive bipartite consensus control of high‐order multiagent systems on coopetition networks

In this paper, a bipartite consensus problem is considered for a high‐order multiagent system with cooperative‐competitive interactions and unknown time‐varying disturbances. A signed graph is used to describe the interaction network associated with the multiagent system. The unknown disturbances are expressed by linearly parameterized models, and distributed adaptive laws are designed to estimate the unknown parameters in the models. For the case that there is no exogenous reference system, a fully distributed adaptive control law is proposed to ensure that all the agents reach a bipartite consensus. For the other case that there exists an exogenous reference system, another fully distributed adaptive control law is also developed to ensure that all the agents achieve bipartite consensus on the state of the exogenous system. The stability of the closed‐loop multiagent systems with the 2 proposed adaptive control laws are analyzed under an assumption that the interaction network is structurally balanced. Moreover, the convergence of the parameter estimation errors is guaranteed with a persistent excitation condition. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed adaptive bipartite consensus control laws for the concerned multiagent system.     

Cooperative output regulation of heterogeneous multi-agent systems based on passivity

This paper investigates the problem of cooperative output regulation of heterogeneous linear multi-agent systems. A passive framework is presented for the stabilisation analysis of cooperative output regulation, which can overcome the difficulty caused by the fact that the global dynamics of heterogeneous multi-agent systems depends on the global communication structure. An adaptive distributed observer is proposed to estimate the state of the exosystem, and the proposed distributed observer is independent of any global information of the communication graph. Based on passivity design and adaptive distributed observer, both a distributed state feedback and a distributed output feedback protocol are designed for output synchronisation of heterogeneous multi-agent systems. The gain matrices of the distributed protocols and observers are obtained by a Riccati equation design approach. Furthermore, sufficient local conditions for solving the problem of cooperative output regulation of heterogeneous multi-agent systems are presented. Finally, numerical simulation results are given to illustrate the effectiveness of the proposed distributed control schemes.     

Cooperative global output regulation of heterogeneous second-order nonlinear uncertain multi-agent systems

In this paper, we study the cooperative global output regulation problem for a class of heterogeneous second order nonlinear uncertain multi-agent systems. We first introduce a type of distributed internal model that converts the cooperative global output regulation problem into the global robust stabilization problem of the so-called augmented multi-agent system. Then we further globally stabilize this augmented multi-agent system via a distributed state feedback control law, thus leading to the solution of the original problem. A special case of our result leads to the solution of the global leader-following consensus problem for the second order nonlinear multi-agent systems without satisfying the global Lipschitz condition.