Intelligent Optimal Input-Constrained Control with Model Uncertainty and External Disturbance Applied to Robotic Manipulators

This paper investigates a novel critic optimal input-constrained control strategy with disturbance attenuation for robotic manipulators subject to model perturbation and external disturbance. Drawing upon the framework of a two-player zero-sum differential game initially designed to handle external disturbance, we incorporate an auxiliary boundary function into the performance index function to tackle model uncertainty. Notably, a nonquadratic utility is adopted to compensate for input saturation. Moreover, the approximate optimal control policy and the worst-case disturbance law are solved through the enhanced adaptive critic algorithm with an additional stabilizing component. Using Lyapunov theory, the stability of the system state and the convergence of network weight are thoroughly proven. Finally, comparative numerical simulation and Monte Carlo test are implemented to validate the effectiveness of the presented approach.