Approximate dynamic programming, also known as reinforcement learning, is applied for optimal control of Antilock Brake Systems (ABS) in ground vehicles. As an accurate and control oriented model of the brake system, quarter vehicle model with hydraulic brake system is selected. Due to the switching nature of hydraulic brake system of ABS, an optimal switching solution is generated through minimizing a performance index that penalizes the braking distance and forces the vehicle velocity to go to zero, while preventing wheel lock-ups. Towards this objective, a value iteration algorithm is selected for ‘learning’ the infinite horizon solution. Artificial neural networks, as powerful function approximators, are utilized for approximating the value function. The training is conducted offline using least squares. Once trained, the converged neural network is used for determining optimal decisions for the actuators on the fly. Numerical simulations show that this approach is very promising while having low real-time computational burden, hence, outperforms many existing solutions in the literature.
Sub-optimal switching in anti-lock brake systems using approximate dynamic programming
