Adaptive Distributed Model Predictive Contouring Control for Path Following of Unmanned Surface Vessels

This paper addresses the challenges of model mismatch and hyperparameter sensitivity in path following control for multiple unmanned surface vehicles (USVs) operating in dynamic marine environments. We propose a synergistic framework integrating a Multi-Innovation Extended Kalman Filter (MI-EKF) and a Score-Driven Trust-Region Bayesian Optimization (Score-Driven TuRBO) to enhance the performance of Distributed Model Predictive Contouring Control (DMPCC). First, the MI-EKF algorithm dynamically estimates unmodeled dynamics by fusing multi-innovation observation residuals, enabling online compensation for the USV's dynamic model used in DMPCC. Second, an improved Trust-Region Bayesian Optimization is developed with a dynamic scoring mechanism that adaptively allocates candidate points and adjusts scaling factors based on historical improvement rates, achieving efficient global optimization of DMPCC hyperparameters. Comparative simulations validate the efficacy of the proposed method.