Reservoir Computing-Enhanced Tube-MPC: Real-Time Self-Healing Control for Robust AUV Path Following Under Dynamic Faults

Abstract

This paper presents a novel control framework that integrates reservoir computing (RC) with Tube model predictive control (Tube-MPC) for robust path following in quadrotor autonomous underwater vehicles (QAUVs) under sudden fault conditions. The proposed RC-Tube-MPC leverages the dynamic modeling capabilities of RC to efficiently approximate complex nonlinear behaviors, while Tube correction ensures robust performance despite model uncertainties and external disturbances. Comparative simulations demonstrate that RC-Tube-MPC outperforms alternative approaches in terms of path following accuracy and computational efficiency. Additionally, the influence of training data length on learning performance is analyzed, revealing that the proposed method maintains superior performance across various data regimes. Notably, in severe fault scenarios, such as a fault factor of 0.3, RC-Tube-MPC uniquely restores convergence to the reference path. These results underscore the potential of the integrated RC-Tube-MPC approach for real-time control applications in dynamic, fault-prone underwater environments.