Sliding Optimal Tracking Control of Autonomous Underwater Vehicles With Adaptive Dynamic Programming
Baixue Miao, Yongfeng Lv, Huimin Chang, Xuemei Ren
Abstract
To improve the tracking performance of autonomous underwater vehicles (AUVs), a sliding optimal tracking control method for linear continuous systems is proposed with adaptive dynamic programming. The AUV vertical dynamic model is constructed with the kinematic principle first. The tracking model is then transformed into a linear sliding model, and the optimal feedback tracking controller is established. Considering the tracking performance of the AUV and the transient performance of the AUV, an adaptive Riccati equation is linearly parameterized, and an online learning algorithm driven by a parameter estimation error is introduced to study the optimal solution of the algebraic Riccati equation. Finally, the stability of the closed-loop system and the estimation convergence of the sliding model are proved by Lyapunov theory. Simulation results demonstrate that the proposed method effectively achieves sliding optimal tracking performance with good dynamic response, high tracking accuracy, and robustness.