Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions

Abstract

This article investigates the parallel trajectory tracking control of fully-actuated maritime autonomous surface ships (MASSs) in the presence of multiple stationary/moving ocean obstacles. A safety-critical parallel control architecture is proposed for the trajectory tracking control of MASSs. Specifically, an artificial MASS system is constructed based on a data-driven learning predictor where real-time and historical navigation data are both utilized to achieve the estimation of the unknown weights of Taylor polynomials and Fourier series. Then, a parallel trajectory tracking control law is designed based on the artificial system such that the MASS is capable of track the reference trajectory positively. Finally, integral control barrier functions are employed to encode input and safety constraints. A safety optimization signal is augmented to the designed parallel control law to achieve the collision avoidance of all ocean obstacles. Based on the stability and safety analyses, the tracking errors of the actual MASS system are verified to be uniformly ultimately bounded and the MASS system is safe. Numerical examples confirm the effectiveness of the designed safety-critical parallel trajectory tracking control scheme for the MASS.