SMC-based model-free tracking control of unknown autonomous surface vehicles

Abstract

This paper investigates the robust tracking control problem of disturbed unknown autonomous surface vehicles (ASVs), and whereby a sliding-mode-control-based model-free tracking control (SMTC) approach by the combination of sliding-mode control and data-driven backstepping techniques is innovatively devised. By deploying a data-driven backstepping sliding-mode surface, a robust model-free adaptive controller is designed to achieve strong adaptability and robustness to unknown couplings, uncertainties and disturbances. Besides, a data-driven adaptive law based on disturbance observer and feedforward control strategies is effectively developed to estimate these unknowns, and thereafter the estimation is served as the compensation within the controller. Rigorous analysis proves that asymptotic tracking performance and strong robustness can be guaranteed theoretically. Lastly, simulation studies for the ASV are explored to demonstrate the validity and superiority of the devised SMTC approach in terms of disturbance attenuation, nonlinearity adaption, and high accurate tracking.