Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning
研究全驱动水下自主航行器在水平面运动时，考虑外部干扰、控制输入非线性和模型不确定性的轨迹跟踪问题，提出一种集成评价器和动作神经网络的离散时间自适应控制方法，并通过仿真验证其鲁棒性和有效性。
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV's control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.