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针对异构非线性多智能体系统(Multi-agent system, MAS)的输出一致性控制难题,设计了一种基于同胚分布式控制协议的无模型方法.通过将输出反馈线性化理论与自适应动态规划相结合,可以在不需要精确系统模型的情况下实现非线性智能体的线性化,简化分布式控制器的设计复杂性.具体而言,设计一种双层分布式控制结构,在物理空间层通过无模型反馈线性化方法实现未知系统线性化,在微分同构空间层利用线性控制技术进行分布式共识控制.通过两个实验验证了所提方法在处理未知异构非线性多智能体系统中的有效性,将传统的线性分布式控制方法扩展到未知非线性多智能体系统的控制器设计. 相似文献
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针对一类邻居控制信息未知,且节点只能获得相对输出信息的多智能体系统,研究基于未知输入观测器的分布式故障检测问题,以实现节点对自身及邻居故障的实时检测.首先,通过对节点动力学模型进行分解与变换,构造出基于相对输出信息的故障检测参考模型,并给出未知输入观测器的存在性证明;接着,设计不依赖邻居节点控制信息的未知输入观测器,突破控制信息缺失导致观测器失效的理论难题;最后,借助未知输入观测器设计故障检测算法,并完成算法的分布式实现.仿真结果验证了所提方法的有效性与先进性. 相似文献
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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． 相似文献
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在有向通讯拓扑图下,针对一类具有输出约束和执行器偏差增益故障的非严格反馈随机多智能体系统,提出一种自适应神经网络容错控制设计方案.采用神经网络逼近未知非线性函数,构造障碍李雅普诺夫函数处理系统的输出约束问题,以反步法和动态面技术为框架,结合Nussbaum函数设计自适应神经网络容错控制方法.基于李雅普诺夫稳定性理论,证明所有跟随者输出与领导者输出达到一致,闭环系统的所有信号依概率半全局一致最终有界且系统输出限制在给定紧集内.论文最后通过仿真实验验证所给出控制方案的有效性. 相似文献
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不确定非线性多智能体系统的分布式容错协同控制 总被引：1，自引：0，他引：1
针对一类存在未知非线性的多智能体系统,研究具有执行器故障的“领导-跟随”协同控制问题。利用模糊逻辑系统逼近系统的未知非线性,通过设计故障估计器辨识系统的故障。在“跟随者”之间的通信网络为单向连通的情况下,提出分布式模糊容错协同控制器的设计方案,实现“跟随者”的状态跟踪“领导者”的状态。基于Lyapunov稳定性理论,证明系统的跟踪误差一致最终有界。仿真结果验证了所提出设计方法的有效性。 相似文献
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针对离散时间多智能体系统的协同最优输出调节问题,在不依赖多智能体系统矩阵精确信息的条件下提出分布式数据驱动自适应控制策略.基于自适应动态规划和分布式自适应内模,通过引入值迭代和策略迭代两种强化学习算法,利用在线数据学习最优控制器,实现多智能体系统的协同输出调节.考虑到跟随者只能访问领导者的估计值进行在线学习,对闭环系统的稳定性和学习算法的收敛性进行严格的理论分析,证明所学习的控制增益可以收敛到最优控制增益.仿真结果验证了所提控制方法的有效性. 相似文献
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综述了多智能体系统分布式一致性问题的研究现状。从理论层面介绍了一致性问题的几种常见定义及与特性相关的主要参数;总结归纳了近年来几种一致性协议及其理论分析结果;分析和阐述了一致性问题的主要应用领域的进展。展望了未来的研究方向。 相似文献
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This paper introduces output feedback distributed optimization algorithms designed specifically for second-order nonlinear multi-agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as long as they satisfy the Lipschitz continuity condition. For the case with unknown states, nonlinear state observers are designed first for each agent to reconstruct agents' unknown states. It is proven that the agents' unknown states are estimated accurately by the developed state observers. Then, based on the agents' state estimates and the gradient of each agent local cost function, a kind of output feedback distributed optimization algorithms are proposed for the considered multi-agent systems. Under the proposed distributed optimization algorithms, all the agents' outputs asymptotically approach the minimizer of the global cost function which is the sum of all the local cost functions. By using Lyapunov stability theory, convex analysis, and input-to-state stability theory, the asymptotical convergence of the output feedback distributed optimization closed-loop system is proven. Simulations are conducted to validate the efficacy of the proposed algorithms. 相似文献
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In this paper, we propose a new universal output feedback adaptive controller to globally (or semiglobally) stabilize nonlinear output feedback systems, whose nonlinearities are bounded either by known functions with unknown parameters or by completely unknown functions. In addition, no a priori knowledge of the sign of high‐frequency gain is required. The new design focuses on properly arranging the control gains step by step in the filter backstepping design. Instead of Lyapunov‐based argument, an inductive contradiction argument is employed in the proof of stability, which is not common in literature. 相似文献
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本文研究了具有输出非对称死区和状态含未知控制方向的非严格反馈非线性系统, 设计了稳定的自适应神经网络控制器. 首先, 针对输出非对称死区的问题, 本文采用死区逆的方法, 构造光滑模型逼近原死区模型. 其次, 在控制器设计过程中, 基于障碍Lyapunov函数的构造, 动态面控制和反步法, 设计出自适应控制信号, 虚拟控制信号和实际控制信号. 通过稳定性分析, 证明所设计的神经网络控制器可以保证闭环系统内所有信号是半全局一致最终有界. 最后, 通过MATLAB数值仿真, 说明所设计控制器的有效性. 相似文献
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This paper considers global output feedback stabilization via sampled‐data control for a general class of nonlinear systems, which admit unknown control coefficients and nonderivable output function. A sector region of the output function is given by utilizing a technical lemma, and a sampled‐data controller is designed by combining a robust state stabilizer and a reduced‐order sampled‐data observer. By carefully choosing an appropriate sampling period, the proposed controller guarantees the globally asymptotical stability of the closed‐loop systems. 相似文献
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An adaptive output feedback control approach is studied for a class of uncertain nonlinear systems in the parametric output feedback form. Unlike the previous works on the adaptive output feedback control, the problem of ‘explosion of complexity’ of the controller in the conventional backstepping design is overcome in this paper by introducing the dynamic surface control (DSC) technique. In the previous schemes for the DSC technique, the time derivative for the virtual controllers is assumed to be bounded. In this paper, this assumption is removed. It can be proven that the resulting closed‐loop system is stable in the sense that all the signals are semi‐global uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to verify the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Motivated by the future use of embedded microprocessors with limited resources and limited computational resources, the distributed output regulation with event-driven strategies problem of linear multi-agent systems is considered in this paper. The main task is to design distributed feedback by employing event-triggered technique for multi-agent systems such that all agents can track an active leader, and/or distributed disturbance rejection. Both leader and disturbance are generated by some external system (exosystem). Both distributed static and dynamic feedback with event-triggered strategy are constructed here. Then, the input-to-state stability of the closed-loop multi-agent system is analysed. Finally, a numerical example is given to validate the proposed control. 相似文献
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This article focuses on the distributed consensus control problem for nonlinear multi-agent systems subject to sensor uncertainty. To be specific, we study nonlinear multi-agent systems of lower or upper triangular structure with unknown growth rate and sensor uncertainty. A new time-varying gain approach is proposed to construct observers as well as distributed output-feedback controllers. By selecting suitable design parameters, the leader-follower consensus of nonlinear multi-agent systems is achieved. Different from the existing results, a time-varying function in a logarithmic form is introduced to deal with unknown growth rate. Moreover, a monotonically increasing time-varying function is constructed to cope with uncertain sensor sensitivity. Two simulation examples are provided to demonstrate the effectiveness of the proposed distributed consensus control algorithms. 相似文献
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This paper addresses the global stabilization via adaptive output‐feedback for a class of uncertain nonlinear systems. Remarkably, the systems under investigation are with multiple uncertainties: unknown control directions, unknown growth rates and unknown input bias, and can be used to describe more physical plants. Multiple uncertainties, which usually cannot be compensated by a sole compensation technique, may give rise to big technical difficulty for controller design. To overcome such difficulty and to achieve the global stabilization, a new adaptive output‐feedback scheme is proposed in this paper, by flexibly combining Nussbaum‐type function, tuning function technique and extended state observer. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non‐zero set‐point regulation is given to demonstrate the effectiveness of the theoretical results. 相似文献
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In this article, an optimal command-filtered backstepping control approach is proposed for uncertain strict-feedback nonlinear multi-agent systems (MASs) including output constraints and unmodeled dynamics. One-to-one nonlinear mapping (NM) is utilized to recast constrained systems as corresponding unrestricted systems. A dynamical signal is applied to cope with unmodeled dynamics. Based on dynamic surface control (DSC), the feedforward controller is designed by introducing error compensating signals. The optimal feedback controller is produced applying adaptive dynamic programming (ADP) and integral reinforcement learning (IRL) techniques in which neural networks are utilized to approximate the relevant cost functions online with established weight updating laws. Therefore, the entire controller, including feedforward and feedback controllers, not only ensures that all signals in the closed-loop systems are cooperative semi-globally uniformly ultimately bounded (SGUUB) and the outputs maintain in the provided time-varying constraints, but also makes sure that the cost functions achieve minimization. A simulation example is presented to illustrate the feasibility of the proposed control algorithm. 相似文献