系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (4): 1220-1229.doi: 10.12305/j.issn.1001-506X.2022.04.19

• 系统工程 • 上一篇    下一篇

具有混合执行器故障的多智能体分布式有限时间自适应协同容错控制

张普*, 薛惠锋, 高山, 左轩   

  1. 西北工业大学自动化学院, 陕西 西安 710129
  • 收稿日期:2021-06-30 出版日期:2022-04-01 发布日期:2022-04-01
  • 通讯作者: 张普
  • 作者简介:张普(1990—), 女, 博士研究生, 主要研究方向为多智能体协同容错跟踪控制|薛惠锋(1964—), 男, 教授, 博士研究生导师, 博士后, 主要研究方向为复杂系统与系统工程|高山(1988—), 女, 博士研究生, 主要研究方向为航天产品样机性能研究|左轩(1989—), 男, 博士研究生, 主要研究方向为多智能体系统控制与机器学习
  • 基金资助:
    西北工业大学博士论文创新基金(CX2021086);国家留学基金(202006290178)

Distributed finite-time adaptive cooperative fault-tolerant control for multi-agent systems with integrated actuators faults

Pu ZHANG*, Huifeng XUE, Shan GAO, Xuan ZUO   

  1. School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
  • Received:2021-06-30 Online:2022-04-01 Published:2022-04-01
  • Contact: Pu ZHANG

摘要:

针对多智能体编队系统执行器发生故障时, 所引起的参数不确定以及系统瞬态不稳定问题, 本文采用径向基函数神经网络(radial basis function neural networks, RBFNNs)对不确定参数(未知函数)进行估计。同时, 基于反推技术设计出合理的自适应容错控制器, 并通过有限时间理论保证系统实现瞬态稳定。首先, 本文采用10个智能体作为被控对象, 基于有向通讯拓扑结构理论, 构建了非线性多智能体系统模型。其次, 基于RBFNNs逼近特性, 采用反推技术与动态面技术相结合, 设计出合理的容错控制器, 补偿多智能体中出现的未知非线性执行器故障, 并采用有限时间理论解决系统瞬态不稳定问题。接着, 基于Lyapunov稳定性理论分析了控制器的稳定性和快速收敛性。最后, 通过两种算例对比, 验证了所设计的控制器性能优于传统的反推技术, 为工程实践提供了一种有效的研究思路。

关键词: 多智能体系统, 混合执行器故障, 协同容错控制, 径向基函数神经网络, 动态面技术

Abstract:

Aiming at the uncertain parameters and transient instability of the system caused by the actuator fault for the multi-agent formation system, this paper adopts the radial basis function neural networks (RBFNNs) to approximate the uncertain parameters (the unknown nonlinear function). A reasonable adaptive fault-tolerant controller is designed based on the back-stepping technology, and the transient stability of the system is guaranteed by the finite time theory. Firstly, this paper adopts ten agents as the controlled objects, and constructs a nonlinear multi-agent system model based on the theory of directed communication topology. Secondly, based on the approximation characteristics of the RBFNNs, a reasonable fault-tolerant controller is designed by combining the back-stepping technology and the dynamic surface technology to compensate the unknown nonlinear actuator faults in the multi-agent, and the finite-time theory is used to solve the system transient state instability problem. Then, the stability and fast convergence of the controller are analyzed based on the Lyapunov stability theory. Finally, through the comparison of two calculation examples, it is verified that the performance of the designed controller is better than the traditional back-stepping technology, which provides an effective research idea for engineering practice.

Key words: multi-agent system, integrated actuator fault, cooperative fault-tolerant control, radial basis function neural networks (RBFNNs), dynamic surface technology

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