Hand Position-Based L1-Model Predictive Control for Robust Trajectory Tracking of Underactuated Surface Vehicles

Abstract

This study presents a control strategy that integrates L1 adaptive control with model predictive control (MPC) to achieve robust trajectory tracking for underactuated surface vehicles in the presence of modeling inaccuracies and environmental disturbances. In this work, MPC serves as the baseline controller for nominal trajectory tracking, and L1 adaptive control addresses uncertainties considering the nonlinear dynamics of the vehicle. Although L1 adaptive control is robust against uncertainties, its effectiveness in nonlinear systems is limited to cases with matched uncertainties. Addressing unmatched uncertainties, particular those associated with the sway dynamics of underactuated surface vehicles, remains a control challenge. To address this challenge, the concept of the hand position is utilized to modify the control point where only matched uncertainties exist. The effectiveness and practicality of the proposed control approach has been validated through numerical simulator and field experiments using an unmanned surface vehicle.

Keywords: Model predictive control, L1 adaptive control, Hand position