Development of fault detection, classification, and fault-tolerant control algorithms based on sliding mode approach for 4WISD vehicles

To prevent fatal vehicle accidents under various driving conditions, it is essential to develop a fail-safe system that integrates hardware and software. In this paper, fault detection, classification, and fault-tolerant steering algorithms are proposed based on the sliding mode approach and relative error method to deal with driving system faults in four-wheel independent steering and driving (4WISD) vehicles. First, a sliding mode observer (SMO) is designed to estimate the uncertainty of driving systems using an injection term, and the relative errors are computed using the injection term for fault detection. The injection term is used for recursive least squares (RLS) to estimate the longitudinal tire force and the differences in the damping coefficient and wheel torque. Second, a fault classification algorithm is designed to identify driving system faults based on the differences in the calculated relative errors. Third, the sliding mode control (SMC) is designed with the finite-time stability condition to compute the steering control inputs of each wheel for fault-tolerant steering. A control allocation rule consisting of four proportional constants is designed based on Lyapunov stability conditions. The performance was evaluated via co-simulation using the MATLAB/Simulink and CarMaker software to verify the performance of the proposed algorithms under a step steering scenario with two fault conditions.