Abstract
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.
Highlights
Significant advances towards vehicle safety have been made with one such system being the development of the Antilock Braking System (ABS)
Majority of ABS control approaches rely on a set of rules and are robust on specific smooth terrains, this does not hold over rough terrain where mismatches tend to occur between the control design model and the real process [5], and as a result, the un-modelled dynamics and parametric uncertainties lead to underwhelming performances of the ABS
It can be seen that the Double Deep Q-learning (DDQN)-Temporal Convolutional Network (TCN) algorithm cycles the wheel speed better than the Bosch algorithm, within the same amount of step intervals, hereby achieving small slippage as confirmed in Figure 5, where a maximum slip of 50% is achieved compared to 100% for the Bosch algorithm
Summary
Significant advances towards vehicle safety have been made with one such system being the development of the Antilock Braking System (ABS). Majority of ABS control approaches rely on a set of rules (a defined model) and are robust on specific smooth terrains (dry, icy, or wet), this does not hold over rough terrain where mismatches tend to occur between the control design model and the real process [5], and as a result, the un-modelled dynamics and parametric uncertainties lead to underwhelming performances of the ABS. In [7] the use of ADP for the optimal control of an ABS over smooth terrain is proposed This approach relies on penalizing the braking distance of the vehicle, and outperforms many existing solutions in literature. Developing a model-free ABS controller which prevents wheel-lockup on rough terrain without deteriorating the stopping distance forms the main objective of this study
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