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

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 modelfree 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.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the problem of robust tracking control of disturbed unknown autonomous surface vehicles (ASVs). A sliding-mode-control-based model-free tracking control (SMTC) approach is proposed, which combines sliding-mode control and data-driven backstepping techniques. The approach includes a data-driven adaptive controller and a data-driven adaptive law for estimating unknowns. The proposed SMTC approach achieves strong adaptability and robustness to unknown couplings, uncertainties, and disturbances, and guarantees asymptotic tracking performance and strong robustness theoretically. Simulation studies demonstrate the validity and superiority of the SMTC approach in terms of disturbance attenuation, nonlinearity adaption, and high accurate tracking.