PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems

Nowadays, the antilock braking system (ABS) is the standard in all modern cars. The function of ABS is to optimize the maximize wheel traction by preventing wheel lockup during braking, so it can help the drivers to maintain steering maneuverability. In this study, a self-organizing interval type-2 fuzzy neural network (SOT2FNN) control system is designed for antilock braking systems. This control system comprises a main controller and a robust compensation controller; the SOT2FNN as the main controller is used to mimic an ideal controller, and the robust compensation controller is developed to eliminate the approximation error between the main controller and the ideal controller. To guarantee system stability, adaptive laws for adjusting the parameters of SOT2FNN based on the gradient descent method are proposed. However, in control design, the learning rates of adaptive law are very important and they significantly affect control performance. The particle swarm optimization method is therefore applied to find the optimal learning rates for the weights in reduction layer and also for the means, the variances of the Gaussian functions in the input membership functions. Finally, the numerical simulations of ABS response in different road conditions are provided to illustrate the effectiveness of the proposed approach.