Neurocontroller design and stability analysis for antilock braking systems

Antilock braking system (ABS) controls the slip of each wheel of a vehicle to prevent it from locking such that a high friction is achieved and steerability is maintained. It is designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this study, a neurocontroller system, which is composed of a computation controller and a compensation controller, is developed for ABS. The computation controller containing a radial basis function neural network uncertainty observer is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. Simulations are performed to demonstrate the effectiveness of the proposed neurocontroller system under various road conditions.