OPTIMAL CONTROL OF NONLINEAR INPUT-CONSTRAINTED SYSTEMS BASED ON REINFORCEMENT LEARNING

An adaptive dynamic programming control strategy based on reinforcement learning is designed for optimal tracking control of a class of nonlinear systems with input constraints. An appropriate performance index function was designed to solve the input limitation problem of the control system. An evaluation neural network was designed to estimate the optimal performance index function of the system, and the HJB equation of the control system was solved to obtain the optimal control input. The weight update rate of the evaluation network was obtained by using Lyapunov method, and it was proved that the tracking error of the system and the weight estimation error of the evaluation network were ultimately uniformly bounded (UUB). The optimal utilization numerical simulation results show the effectiveness of the proposed control strategy.