Multi-model predictive control strategy for path-following of unmanned surface vehicles in wide-range speed variations

This paper proposes a robust multi-model predictive control strategy to solve the path-following control problem of unmanned surface vehicles (USVs) in wide-range speed variations. The dynamic characteristics of USVs tend to vary greatly in wide-range speed variations. A discrete piecewise affine system with polytopic uncertainty is established to describe the speed and steering dynamics characteristics of USVs in wide-range speed variations. Then, an improved adaptive line-of-sight (IALOS) guidance law is designed to adapt the USV maneuvering characteristics in different speed ranges, which features a variable look-ahead distance subject to speed and lateral error. Finally, the speed and heading controllers are designed by combining the multi-model switching control theory and the robust model predictive control algorithm based on linear matrix inequalities (LMIs), and the stability proofs are given. Comparative numerical simulations are performed to validate the effectiveness and superiority of the proposed control strategy.