Effective fault-tolerant control paradigm for path tracking in autonomous vehicles

Abstract

A novel fault-tolerant control paradigm that integrates fault detection (FD) with optimal control for path tracking is designed to ensure accurate path tracking in the presence of faults. The proposed approach is designed to maintain vehicle stability, dynamics, and maneuverability in the event of a faulty steering system. A sensor fusion-based fault detection and identification approach is proposed to accurately detect and identify sensor faults when they occur. A weight adjustment algorithm is considered to ensure accurate detection while providing robustness against parameter variations and uncertainties. Following FD and using the estimated fault vector, a fault-tolerant controller is designed to guarantee the stability of the closed loop system. The proposed controller incorporates a linear quadratic regulator (LQR)-based algorithm with a feed-forward gain. The LQR-based controller is designed to maintain system stability under faulty conditions while operating the dynamic system at minimum cost. The proposed approach is validated using a ground vehicle required to track various paths while being subject to multiple fault scenarios. For accurate performance analysis, vehicle handling and dynamics were implemented using CarSim, a high-fidelity vehicle simulator. Effective path tracking capabilities, vehicle handling, and stabilization under both fault-free and faulty conditions are the main positive features of the proposed approach.