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

Yan Zhang (1) , Mohammed Chadli (2) , Zhengrong Xiang (1)

1 NJUST - School of Automation [Nanjing]
2 IBISC - Informatique, BioInformatique, Systèmes Complexes

This paper concerns optimal prescribed-time forma- tion control for a class of nonlinear multi-agent 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 rein- forcement 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.

Keywords: Formation control; formation control; fuzzy logic systems; Hamilton-Jacobi-Bellman; Heuristic algorithms; Multi-agent systems; non- linear multi-agent systems; Optimal control; Performance analysis; prescribed-time control; Protocols; reinforcement learning; Reinforcement learning

Domains: Automatic

Yan Zhang, Mohammed Chadli, Zhengrong Xiang. Prescribed-Time Formation Control for a Class of Multi-agent Systems via Fuzzy Reinforcement Learning. IEEE Transactions on Fuzzy Systems, 2023, 31 (12), pp.4195--4204. ⟨10.1109/TFUZZ.2023.3277480⟩. ⟨hal-04117871⟩