Prescribed-Time Formation Control for a Class of Multiagent Systems via Fuzzy Reinforcement Learning

This article concerns optimal prescribed-time formation control for a class of nonlinear multiagent systems (MASs). Optimal control depends on the solution of the Hamilton–Jacobi–Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the reinforcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.