<div>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.</div>
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.
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