Model-free Predictive Trajectory Tracking Control and Obstacle Avoidance for Unmanned Surface Vehicle With Uncertainty and Unknown Disturbances via Model-free Extended State Observer

Abstract

The present paper proposes a model-free extended state observer (MFESO) based model and model-free predictive control (MPC-MFESO-MFPAC) approach for achieving trajectory tracking and obstacle avoidance of unmanned surface vehicle (USV) in complex environments. MFPAC is investigated for addressing the uncertainty in the kinetics modeling of USV system, eliminating the need for an accurate mathematical model of the USV. Additionally, the backstepping method is employed to eliminate rotational characteristics, enabling direct application of MFPAC in USV control. In the computation of the virtual control law, MPC is utilized for kinematics which can be easily modeled, while incorporating obstacle avoidance performance. By utilizing the model-free ESO for estimating additional unknown perturbations, this approach obviates the need for any prior knowledge of the system’s dynamics. The stability analysis demonstrates that the proposed control strategy is bounded-input and bounded-output (BIBO) stable. The simulation results validate the effectiveness and advancement of the algorithm.