Fixed-Time Optimal Bipartite Containment Control for Stochastic Nonlinear Multiagent Systems With Unknown Hysteresis

摘要

This paper is concerned with the fixed-time optimal bipartite containment control issue for stochastic nonlinear multiagent systems in the presence of unknown Bouc-Wen hysteresis. Initially, under the optimized backstepping framework, the approximation solution of the Hamilton-Jacobi-Bellman equation is obtained via the reinforcement learning algorithm. Then, the neural networks are utilized to shape an identifier-critic-actor structure to approximate unknown dynamics, evaluate system performance, and implement control behavior, respectively. Moreover, an intermediate variable is devised to cope with the unknown control coefficient resulting from Bouc-Wen hysteresis. Theoretical analysis shows that all states of the closed-loop system are semi-global practical fixed-time stable, and its convergence time is capable of adjusting by choosing appropriate design parameters. Finally, a numerical example is performed to solidify the effectiveness of the proposed strategy. Note to Practitioners—It is well known that fixed-time convergence is one of the significance performance metrics in many industrial systems including manipulator systems, robotic systems as well as unmanned aerial systems. Motivated by this fact, this article devotes the first attempt to investigate the fixed-time optimal bipartite containment control issue for stochastic nonlinear multiagent systems in the presence of unknown Bouc-Wen hysteresis according to the reinforcement learning control strategy. Theoretical analysis and simulation examples both show that all state signals of the closed-loop system are semi-global practical fixed-time stable, and its convergence time is capable of adjusting by choosing appropriate design parameters.