Adaptive Fault-tolerant Control of Underwater Vehicles with Thruster Failures

TL;DR

This work addresses robust trajectory tracking for AUVs under thruster failures by formulating a bank of failure models and applying a Bayesian, soft-switching approach to fuse model-specific LQT controllers. The method combines EKF-based state estimation with Bayesian posterior weighting to achieve smooth transitions between fault modes without relying on explicit fault-detection, and introduces a lock-in/unlock mechanism to prevent stagnation in model identification. Theoretical development includes a discrete-time linearized AUV model, LQT controller synthesis for each fault model, and a probabilistic framework that updates model posteriors to weight controller outputs. Numerical simulations on a BlueROV2 Heavy demonstrate rapid fault-model identification, reduced control effort during switching, and sustained three-dimensional trajectory tracking despite multiple thruster faults, underscoring practical relevance for reliable underwater operations.

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.

Figures (14)

• Figure 1: Solution for fault-tolerant control in thrust reconstruction methods. The dotted line represents the transfer of AUV thruster modes. Each of the modes corresponds to a set of feasible solutions for fault-tolerant control. The optimal control solution is determined by the AUV mode and the intersection of modes.
• Figure 2: The two respective coordinate systems of the Blue ROV2 Heavy
• Figure 3: Structure of the proposed adaptive fault tolerant control (LQT: Linear quadratic tracking. EKF: Extended Kalman Filter. $u$ represents the control signal, $k$ is the time instant in the control, $p_{i}$ is the posteriori probability at time instant $i$, $\Lambda_k$ is the historical observations from the AUV.)
• Figure 4: Block diagram for LQT control for the AUV
• Figure 5: Thrusters on a BlueROV Heavy AUV.