Anti-lock braking systems data-driven control using Q-learning

A model-free tire slip control solution for a fast, highly nonlinear Anti-lock Braking System (ABS) is proposed in this work via a reinforcement Q-learning optimal control approach. The solution is tailored around a batch neural fitted scheme using two neural networks to approximate the value function and the controller, respectively. The transition samples are collected from the process through interaction by online exploiting the current iteration controller (or policy) under an ε-greedy exploration strategy. The ABS process fits this type of learning-by-interaction since it does not need an initial stabilizing controller. The validation case studies carried out on a real laboratory setup reveal that high control system performance can be achieved after several tens of interaction episodes with the controlled process. Insightful comments on the observed control behavior in a set of real-time experiments are offered along with performance comparisons with several other controllers.