A Hierarchical Distributed Data-Driven Adaptive Learning Control for Nonaffine Nonlinear MASs
Yongsheng Ma, Wei‐Wei Che
Abstract
This article designs a new hierarchical distributed data-driven adaptive learning control algorithm to accomplish the leader-following tracking control objective for nonaffine nonlinear multiagent systems (MASs). The proposed hierarchical control structure is composed of a distributed observer and a decentralized data-driven adaptive learning controller. Considering that some followers cannot directly receive information from the leader, a distributed observer is designed to estimate the information of the leader. Based on this, a decentralized data-driven adaptive learning controller is further devised to enable the follower to track the estimated information of the leader, where the model parameter learning algorithm is developed to capture the dynamic characteristics of the original system. One advantage of the developed hierarchical control learning algorithm is that neither the leader's system model nor the follower's system model is needed. The other one is the elimination of the noncausal problem without the additional assumption. Simulation results exemplify the merits of the theoretical results by comparisons.