Multiple sensor fault-tolerant predictive control for autonomous surface vehicle formation

Artikel i vetenskaplig tidskrift, 2025

To address the problem of cooperative Autonomous Surface Vehicle (ASV) formation control under multiple sensor faults (e.g., abnormal positioning and speed data), this paper proposes a predictive fault-tolerant control approach based on the Distributed Extended Kalman Filter (DEKF) and Model Predictive Control (MPC), termed DEKF-MPC. An ASV formation path following model is established based on parameterized paths and virtual leader–follower structure, and the ASVs’ cooperative objective for formation is transformed into a specific trajectory tracking task for each ASV. Moreover, a DEKF state estimator based on Statistical Analysis with Random Sample Consensus EKF (SAR-EKF) and Short Term Memory EKF (STM-EKF) is designed by integrating multiple sensor faults into the DEKF process model and taking auxiliary data from fault-free ASVs as inputs to DEKF, to estimate the ASVs’ position, Speed Through Water (STW), and current speed. Then, the DEKF-MPC formation path following fault-tolerant controller is designed with a defined cooperative control objective function, estimated states, and multiple constraints. The simulation results show that the proposed DEKF-MPC approach outperforms the Adaptive Particle Filter-MPC (APF-MPC) and Random Sample Consensus-EKF-MPC (RANSAC-EKF-MPC) methods. It enables ASVs to accurately track target trajectories and maintain consistent headings, especially in the presence of multiple sensor faults.