基于Huber函数非线性模型预测控制的自主水下机器人自适应轨迹跟踪控制方法OA

Achieving stable and high-precision motion control of autonomous underwater vehicles(AUVs)faces severe challenges such as complex external environmental disturbances and internal dynamic coupling interference.To solve the horizontal planar trajectory tracking problem of AUVs under these complex disturbances,a novel joint control algorithm was proposed based on Huber-function-focused nonlinear model predictive control(HNMPC)and an adaptive extended state observer(AESO).Addressing the critical issue of sudden mutational disturbances,the HNMPC method was developed,where the introduced Huber penalty function effectively enhanced the system robustness at steady state.Meanwhile,an extended state observer(ESO)was employed to actively handle the unmodeled dynamics and unknown lumped disturbances of the AUV.To effectively mitigate the peaking phenomenon and high-frequency oscillations inherent in traditional ESOs,the AESO was proposed,which successfully reduced the initial state transient peaks and improved steady-state control precision.The performance of the proposed algorithm was firstly validated within the Gazebo simulation platform.Simulation results in a complex sinusoidal trajectory tracking task under simulated ocean current disturbances demonstrated that the proposed algorithm reduced the integral absolute error(IAE)in the surge,sway,and yaw directions by 41%,35.09%,and 40.78%,respectively,compared with the NMPC+ESO algorithm;its advantages were further demonstrated through comparative evaluations with traditional PID and standard NMPC algorithms.Finally,a physical pool experiment platform was established for real-world validation.The experimental results showed that the proposed algorithm reduced the IAE in the surge,sway,and yaw directions by 41.22%,35.54%,and 0.79%,respectively,compared with the NMPC+ESO algorithm,thoroughly validating the effectiveness and practical applicability of the proposed method in real-world environments.