Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems
研究了状态约束非线性严格反馈系统的分布式模糊最优一致性控制问题，提出一种标识器-执行器-评价器架构，通过模糊标识器逼近未知动态，利用两个模糊逻辑系统分别执行控制和评价性能，使所有智能体达成一致且满足状态约束，同时达到纳什均衡。
This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor-critic architecture. First, a fuzzy identifier is designed to approximate each agent's unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.