Global Exponential Tracking Control for an Autonomous Surface Vessel: An Integral Concurrent Learning Approach

In this paper, an adaptive controller is developed for a fully actuated marine vehicle where the rigid body and hydrodynamic parameters are unknown. A data-based integral concurrent learning method is used to compensate for the uncertain parameters. A Lyapunov-based analysis is presented to show that the closed-loop system is globally exponentially stable and the uncertain parameters are identified exponentially without the requirement of persistence of excitation. Experimental results on an autonomous surface vessel operating on a lake illustrate the controller's ability to track figure-8 trajectories in environments with small disturbances.