Fu Hao, Chen Xin, Wang Wei, Wu Min
IEEE Trans Neural Netw Learn Syst. 2022 Feb;33(2):681-693. doi: 10.1109/TNNLS.2020.3028569. Epub 2022 Feb 3.
This article is concerned with the optimal synchronization problem for discrete-time nonlinear heterogeneous multiagent systems (MASs) with an active leader. To overcome the difficulty in the derivation of the optimal control protocols for these systems, we develop an observer-based adaptive synchronization control approach, including the designs of a distributed observer and a distributed model reference adaptive controller with no prior knowledge of all agents' dynamics. To begin with, for the purpose of estimating the state of a nonlinear active leader for each follower, an adaptive neural network distributed observer is designed. Such an observer serves as a reference model in the distributed model reference adaptive control (MRAC). Then, a reinforcement learning-based distributed MRAC algorithm is presented to make every follower track its corresponding reference model on behavior in real time. In this algorithm, a distributed actor-critic network is employed to approximate the optimal distributed control protocols and the cost function. Through convergence analysis, the overall observer estimation error, the model reference tracking error, and the weight estimation errors are proved to be uniformly ultimately bounded. The developed approach further achieves the synchronization by means of synthesizing these results. The effectiveness of the developed approach is verified through a numerical example.
本文关注具有主动领导者的离散时间非线性异构多智能体系统（MASs）的最优同步问题。为克服推导这些系统最优控制协议的困难，我们开发了一种基于观测器的自适应同步控制方法，包括设计分布式观测器和分布式模型参考自适应控制器，且无需事先了解所有智能体的动态特性。首先，为估计每个跟随者的非线性主动领导者的状态，设计了一种自适应神经网络分布式观测器。该观测器在分布式模型参考自适应控制（MRAC）中作为参考模型。然后，提出一种基于强化学习的分布式MRAC算法，使每个跟随者实时跟踪其行为上的相应参考模型。在该算法中，采用分布式行为-评判网络来逼近最优分布式控制协议和代价函数。通过收敛性分析，证明了整体观测器估计误差、模型参考跟踪误差和权重估计误差一致最终有界。所开发的方法通过综合这些结果进一步实现了同步。通过数值例子验证了所开发方法的有效性。
