Task-oriented adaptive dive tracking control for autonomous underwater vehicle with system uncertainties and disturbances

Achieving high-precision motion control for Autonomous Underwater Vehicles (AUVs) in the vertical plane requires both effective path planning and robust trajectory tracking. Traditional approaches treat these two modules separately, which can lead to infeasible tracking trajectories and suboptimal control performance. To address these limitations, this paper proposes a task-oriented adaptive dive tracking control (ToADTC) framework, which integrates controller constraint into the planning process while simultaneously refining the desired trajectory during trajectory tracking. By defining the optimization problem from the task-level perspective, the desired trajectory is no longer prescribed in advance but can be optimized together with the control input signals within the tracking loop. Moreover, a contracted robustness constraint is incorporated into the optimizing loop, ensuring both the robust control performance and feasibility of the optimization problem. Meanwhile, the Lyapunov method is employed to guarantee the closed-loop stability of the AUV system rigorously. The proposed method is validated through extensive simulations, comparing its performance with conventional control approaches. The results demonstrate that ToADTC method improves robustness against disturbances, and enhances overall control performance in complex underwater environments.