Abstract

This paper considers surge speed tracking control of an autonomous surface vehicle (ASV) model subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. A fully adaptive anti-disturbance control method is proposed for ASV without using any model parameters. Specifically, reduced- and full-order data-driven concurrent learning extended state observers (CLESOs) by utilizing real-time and historical data are designed to estimate the unknown model parameters of the ASV and ensure the convergence of the estimation without requiring persistent excitation. Then, an anti-disturbance surge speed tracking control law is designed. The stability of the data-driven CLESO and surge speed tracking control law are analyzed by using input-state stability (ISS) theory. Simulation results validate the effectiveness of the proposed data-driven CLESO for surge speed tracking of the autonomous surface vehicle with fully unknown dynamic model.

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