Neuro-Fuzzy Control of Antilock Braking System Using Variable-Structure-Systems-Based Learning Algorithm

Abstract

A neuro-fuzzy adaptive control approach for nonlinear systems with model uncertainties is proposed. The implemented control scheme consists of a proportional plus derivative controller that is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line learning algorithm to update the parameters of a neuro-fuzzy feedback controller. The latter is able to gradually replace the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters. An integrating term has been additionally applied to the overall control signal of the two controllers and the performance of the control scheme has been tested on the wheel slip control problem within an antilock breaking system model. The analytical claims have been justified under the existence of model uncertainties and large initial errors.