Advancements have been made in the field of vehicle dynamics, improving the handling and safety of the vehicle through control systems such as the Antilock Braking System (ABS). An ABS enhances the braking performance and steerability of a vehicle under severe braking conditions by preventing wheel lockup. However, its performance degrades on rough terrain resulting in an increased wheel lockup and stopping distance compared to without. This is largely as a result of noisy measurements, and un-modelled dynamics that occur as a result of the vertical and torsional excitation experienced over rough terrain. Therefore, it is proposed that a model-free intelligent technique, which may adapt to these dynamics, be used to overcome this problem. The Double Deep Q-learning (DDQN) technique in conjunction with a Temporal Convolutional Network (TCN) is proposed as the intelligent control algorithm, and straight line braking simulations are performed using a single tyre model, with tyre characteristics approximated by the LuGre tyre model. The rough terrain is modelled after the measured Belgian paving with the normal forces at the tyre contact patch approximated using FTire in ADAMS. Comparisons are drawn against the Bosch algorithm, and results show that the intelligent control approach achieves lateral stability by preventing wheel lockup whilst braking over rough terrain, without deteriorating the stopping distance.
Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain