Zhang Yongwei, Zhao Bo, Liu Derong, Zhang Shunchao
IEEE Trans Neural Netw Learn Syst. 2024 Jul;35(7):9041-9053. doi: 10.1109/TNNLS.2022.3217774. Epub 2024 Jul 8.
This article develops a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems by using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. Subsequently, the DFTCC problem is transformed into an optimal consensus control problem by designing a novel local value function for each agent which contains the estimated fault, the consensus errors, and the control laws of the local agent and its neighbors. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each agent, a critic-only structure is established to obtain the approximate local optimal consensus control law of each agent. Moreover, by using Lyapunov's direct method, it is proven that the approximate local optimal consensus control law guarantees the uniform ultimate boundedness of the consensus error of all agents, which means that all following agents with potential actuator faults synchronize to the leader. Finally, two simulation examples are provided to validate the effectiveness of the present DFTCC scheme.
本文通过使用自适应动态规划为多智能体系统开发了一种分布式容错一致性控制（DFTCC）方法。通过建立局部故障观测器，估计每个智能体潜在的执行器故障。随后，通过为每个智能体设计一个新颖的局部值函数，将DFTCC问题转化为最优一致性控制问题，该局部值函数包含估计的故障、一致性误差以及局部智能体及其邻居的控制律。为了求解每个智能体的耦合汉密尔顿-雅可比-贝尔曼方程，建立了仅含评判器的结构以获得每个智能体的近似局部最优一致性控制律。此外，利用李雅普诺夫直接法证明，该近似局部最优一致性控制律保证了所有智能体一致性误差的一致最终有界性，这意味着所有具有潜在执行器故障的跟随智能体与领导者同步。最后，提供了两个仿真例子来验证所提出的DFTCC方案的有效性。
