Unknown input observer based neuro-adaptive fault-tolerant control for vehicle platoons with sensor fault and output quantization

Sensor fault and output quantization are common issues acting on vehicle platoon, and they may lead to performance deterioration, instability and even insecurity of the platoon. Therefore, this paper investigates the fault-tolerant control (FTC) problem of vehicle platoons with regard to the above two issues. Considering the probabilistic sensor fault and quantized measurement signals, an unknown input observer (UIO) based fault detection algorithm with adaptive threshold is developed for sensor health status monitoring. Then, an augmented vehicle platoon model is constructed by introducing a low-pass output filter, and a robust UIO is established for state reconstruction. Based on the above results, a fault-tolerant control scheme is exploited by employing the back-stepping control method and adaptive radial basis function neural network (RBF NN) approximation technique, which is proved to be capable of achieving the time-domain string stability (TSS) of vehicle platoons in the presence of sensor fault and output quantization. Simulation results demonstrate the effectiveness of the proposed algorithms.