Reduced- and Full-order Concurrent Learning Extended State Observers for Fully Adaptive Anti-disturbance Surge Speed Tracking Control of Autonomous Surface Vehicles

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.