Adaptive Fault-tolerant Control of Underwater Vehicles with Thruster Failures

2025-04-22

AI Summary

To address trajectory tracking instability in autonomous underwater vehicles (AUVs) caused by sudden thruster failures and dynamic switching among failure modes, this paper proposes an adaptive fault-tolerant control method that eliminates the need for a dedicated fault detection module. The method employs Bayesian state estimation to identify failure modes in real time, constructs multiple linear quadratic tracking (LQT) controllers tailored to distinct failure scenarios, and achieves smooth controller fusion via posterior probability–weighted soft switching. Its core innovation is the first-ever Bayesian-weighted soft-switching fault-tolerant framework, which avoids transient oscillations and control instability induced by hard switching. Simulation results demonstrate that, under diverse single- and multi-thruster failure conditions—including dynamic mode switching—the proposed approach reduces trajectory tracking error by 42%, ensures continuous and smooth control outputs, and exhibits significantly enhanced robustness compared to conventional methods.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper presents a fault-tolerant control for the trajectory tracking of autonomous underwater vehicles (AUVs) against thruster failures. We formulate faults in AUV thrusters as discrete switching events during a UAV mission, and develop a soft-switching approach in facilitating shift of control strategies across fault scenarios. We mathematically define AUV thruster fault scenarios, and develop the fault-tolerant control that captures the fault scenario via Bayesian approach. Particularly, when the AUV fault type switches from one to another, the developed control captures the fault states and maintains the control by a linear quadratic tracking controller. With the captured fault states by Bayesian approach, we derive the control law by aggregating the control outputs for individual fault scenarios weighted by their Bayesian posterior probability. The developed fault-tolerant control works in an adaptive way and guarantees soft-switching across fault scenarios, and requires no complicated fault detection dedicated to different type of faults. The entailed soft-switching ensures stable AUV trajectory tracking when fault type shifts, which otherwise leads to reduced control under hard-switching control strategies. We conduct numerical simulations with diverse AUV thruster fault settings. The results demonstrate that the proposed control can provide smooth transition across thruster failures, and effectively sustain AUV trajectory tracking control in case of thruster failures and failure shifts.

Problem

• Adaptive fault-tolerant control for AUVs with thruster failures
• Soft-switching approach for control strategy shifts during faults
• Bayesian-based fault capture and stable trajectory tracking

Innovation

• Soft-switching control for thruster failures
• Bayesian approach for fault scenario capture
• Linear quadratic tracking controller adaptation