Optimal Sliding Mode Control of ROV Fixed Depth Attitude Based on Reinforcement Learning
Wang Fule, Qiuxia Qu, Yuan Baolong, Sun Liangliang, Yupeng Li, Guo Guanyan, Xiao Zupeng, Sun Liang, LI Zhi-gang · 2021
In this paper, an integral sliding mode control algorithm based on reinforcement learning is proposed for underwater vehicle depth determination control system. Since it is difficult for nonlinear continuous systems to track time-varying trajectories, the optimal tracking problem is transformed into a nonlinear time invariant optimal control problem by introducing a new state variable. The HJB equation of nonlinear systems is solved by adaptive dynamic programming (ADP) algorithm to find an approximate optimal strategy. Combined with integral sliding mode control, an approximate optimal sliding mode controller is designed. In addition, the Lyapunov equation is used to verify that the control strategy proposed in this paper can ensure that the tracking error of the system converges to zero gradually, and the error is also verified in a small range. Finally, the effectiveness of the algorithm is verified by simulation experiments, which enhances the anti-interference and robustness of the underwater robot in the depth control direction.