Model-Free Intelligent Control for Antilock Braking Systems on Rough Roads

Advances made in advanced driver assistance systems such as antilock braking systems (ABS) have significantly improved the safety of road vehicles. ABS enhances the braking and steerability of a vehicle under severe braking conditions. However, ABS performance degrades on rough roads. This is largely due to noisy measurements, the type of ABS control algorithm used, and the excitation of complex dynamics such as higher-order tire mode shapes that are neglected in the control strategy. This study proposes a model-free intelligent control technique with no modelling constraints that can overcome these unmodelled dynamics and parametric uncertainties. The double deep Q-learning network (DDQN) algorithm with the temporal convolutional network is presented as the intelligent control algorithm. The model is initially trained with a simplified single-wheel model. The initial training data are transferred to and then enhanced using a validated full-vehicle model including a physics-based tire model, and a three-dimensional (3D) rough road profile with added stochasticity. The performance of the newly developed ABS controller is compared to a baseline algorithm tuned for rough road use. Simulation results show a generalizable and robust control algorithm that can prevent wheel lockup over rough roads without significantly deteriorating the vehicle stopping distance on smooth roads.