Time-Varying Optimal Formation Control for Second-Order Multiagent Systems Based on Neural Network Observer and Reinforcement Learning

Jie Lan, Yan Jun Liu, Dengxiu Yu, Guoxing Wen, Shaocheng Tong, Lei Liu

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

This article addresses a distributed time-varying optimal formation protocol for a class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information and measurable partial states due to actual sensor limitations. In view of the distributed optimized formation strategic needs, the uncertain nonlinear dynamics and undetectable states may jointly affect the stability of the time-varying cooperative formation control. Furthermore, focusing on Hamilton-Jacobi-Bellman optimization, it is almost incapable of directly dealing with unknown equations. Above uncertainty and immeasurability processed by adaptive state observer and NN simplified RL are further designed to achieve desired second-order formation configuration at the least cost. The optimization protocol can not only solve the undetectable states and realize the prescribed time-varying formation performance on the premise that all the errors are SGUUB, but also prove the stability and update the critics and actors easily. Through the above-mentioned approaches offer an optimal control scheme to address time-varying formation control. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and digital simulation.

Original languageEnglish
Pages (from-to)3144-3155
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number3
DOIs
StatePublished - 1 Mar 2024

Keywords

  • Adaptive neural network (NN) observer
  • optimal formation control
  • reinforcement learning (RL)
  • second-order time-varying formation

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