Who Performs Better? A Comparison between Backstepping and Model Predictive Control for Ship Trajectory Tracking

Abstract

This paper aims to solve the trajectory tracking control problem for Autonomous Surface Vessels with nonlinear and cross-coupled models, system constraints, and unknown disturbances. Two advanced control methods, backstepping and model predictive control (MPC), are respectively applied to design controllers for the fully actuated surface vessels. The former represents the mainstream method to deal with nonlinear systems based on Lyapunov theory, and the designed backstepping controller ensures that all signals within the closed-loop trajectory tracking control system are globally uniformly ultimately bounded. The latter is an optimization-based time-domain control method with the distinct feature of systematically handling system constraints. MPC addresses the optimization problem to determine a sequence of actions that steer the autonomous vessel in the correct direction. Only the first action in this sequence is applied, and in the subsequent step, the optimization problem is resolved in a receding horizon fashion. Classical confront newly arising, who will perform better? A series of simulations are conducted to compare the results of the two methods regarding tracking speed, accuracy, constraints handling, and robustness. Consequently, a comprehensive analysis of the advantages and disadvantages of the methods is provided.