Luo Ao, Zhou Qi, Ma Hui, Li Hongyi
IEEE Trans Neural Netw Learn Syst. 2024 Dec;35(12):17281-17291. doi: 10.1109/TNNLS.2023.3301538. Epub 2024 Dec 2.
In this article, an adaptive optimal consensus control problem is studied for multiagent systems (MASs) with external disturbances, unmeasurable states, and prescribed constraints. First, by using neural networks (NNs), a composite observer is constructed to estimate the unmeasurable states and disturbances simultaneously. Then, the consensus error is guaranteed within a prescribed boundary by presenting an improved prescribed performance control (PPC) technique, and the initial conditions for the error are eliminated. In addition, the updating laws of actor-critic NNs are established by using a simplified reinforcement learning (RL) algorithm based on the uniqueness of optimal solution, and the asymmetric input saturation is resolved by designing auxiliary system instead of using nonquadratic cost functions in other optimal control methods. Finally, the boundedness of all signals in the closed-loop system is proved by using Lyapunov stability theory. The effectiveness of the proposed control method is verified by a simulation example.
本文研究了具有外部干扰、不可测状态和规定约束的多智能体系统（MASs）的自适应最优一致性控制问题。首先，利用神经网络（NNs）构造了一个复合观测器，用于同时估计不可测状态和干扰。然后，通过提出一种改进的规定性能控制（PPC）技术，将一致性误差保证在规定边界内，并消除误差的初始条件。此外，基于最优解的唯一性，采用简化的强化学习（RL）算法建立了actor-critic神经网络的更新律，通过设计辅助系统解决了非对称输入饱和问题，而不是像其他最优控制方法那样使用非二次代价函数。最后，利用李雅普诺夫稳定性理论证明了闭环系统中所有信号的有界性。通过仿真例子验证了所提控制方法的有效性。
