ADP-Based H∞ Optimal Control of Robot Manipulators With Asymmetric Input Constraints and Disturbances

Trajectory tracking control for robot manipulators is an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems. Furthermore, the tracking control performance for robot manipulators is greatly affected by input constraints and external disturbances. This paper proposes a novel $ H_{\infty } $ optimal controller for robot manipulators with asymmetric input constraints and external disturbances based on adaptive dynamic programming (ADP). Firstly, a strict feedback nonlinear system is used to represent the robot manipulator dynamics, and then a feedforward controller is designed to construct the tracking error dynamics. Secondly, a value function is introduced, and the Hamilton-Jacobi-Isaacs equation is made and approximated online by the principle of adaptive dynamic programming. Thirdly, the optimal control law and disturbance compensation law are determined. The stability and convergence of the proposed algorithm are analyzed by the Lyapunov technique. Finally, the controller performance is verified through simulation and experimental results with STM32F407 of STMicroelectronics.