Adaptive Dynamic Programming-Based Fixed-Time Optimal Control for Wheeled Mobile Robot

Abstract

In this study, the adaptive dynamic programming (ADP)-based fixed-time optimal trajectory tracking control is investigated for wheeled mobile robots. An ADP-based fixed-time optimal tracking controller is developed based on the critic-only neural network ADP technique, which guarantees the robot track the desired trajectory in fixed time. Firstly, to address the solution difficulty of the Hamilton-Jacobi-Bellman (HJB) equation, a critic neural network is used to estimate the cost function. Meanwhile, a weight update law is designed by using the adaptive control technique, which not only removes the persistent or finite excitation condition, but also enables the fixed-time convergence of the weight estimation error. By using the proposed controller, all error variables can converge to a neighborhood of zero in fixed time. Finally, both simulations and physical experiments indicate that the proposed ADP-based fixed-time optimal controller has a faster convergence rate compared to the two comparison controllers.