Optimized Backstepping Combined With Dynamic Surface Technique for Single-Input–Single-Output Nonlinear Strict-Feedback System
Guoxing Wen, Ranran Zhou, Yanlong Zhao, Ben Niu
Abstract
In this article, for the single-input–single-output (SISO) nonlinear strict-feedback system, optimized backstepping (OB) control combined with the dynamic surface (DS) technique is developed. OB is to make every subsystem control of backstepping as the optimized one so as to ensure the entire backstepping control being optimized. However, the original design of OB still needs to repeatedly calculate the derivative of virtual controls, as a result, it will inevitably cause the problem of “differential explosion.” In order to alleviate the phenomenon, the OB control is combined with the DS technique. Furthermore, OB control needs to conduct with reinforcement learning (RL) in every backstepping step, hence simplifying the algorithm of RL is very necessary and substantive for achieving the combination. In this work, because the optimized control derives both critic and actor training laws by utilizing a simple positive function instead of the square of approximation of Hamilton–Jacobi–Bellman (HJB) equation, it can obviously simplify the RL algorithm to compare with the traditional optimizing methods. Finally, the feasibility is illustrated via both theory and simulation.