Reinforcement Learning Control for a Class of Discrete-Time Non-Strict Feedback Multi-Agent Systems and Application to Multi-Marine Vehicles
Weiwei Bai, Dewang Chen, Bo Zhao, Andrea D Ariano
Abstract
A novel control design problem for a class of non-strict feedback multi-agent systems (MAS) in discrete-time form is studied based on reinforcement learning (RL) and applied to multi-marine vehicles (MMV). Firstly, for this kind of discrete-time MAS, a novel system transformation, which can not only solve the noncausal problem that exists in the backstepping method but also reduce the computational complexity, is proposed. Secondly, the algebraic-loop problem inherent in the conventional controller design is solved by compensating the dynamics and using the property of neural network (NN). Thirdly, the multi-gradient recursive (MGR) RL scheme is developed for the sake of designing the optimal controller. Finally, the stability analysis is presented, and all signals are ensured to be semi-global uniformly ultimately bounded (SGUUB) in the Lyapunov's sense. Besides, this scheme is applied to the MMV which can be described in the non-strict feedback form to extend the application of the designed controller. The MMV simulation demonstrates the validation of this scheme.