Fuzzybased PID with iterative learning active force controller for an antilock brake system

Abstract

Anti-lock braking systems (ABS) are safety and control devices implemented in ground vehicles that prevent the wheel lock-up during panic braking. The existing ABS controls have the ability to regulate the level of pressure to optimally maintain the wheel slip within the vehicle stability range. However, the ABS shows strong nonlinear characteristics in which the vehicles equipped with the existing controllers can still have a tendency to oversteer and become unstable. In this paper, a new intelligent robust control method based on an active force control (AFC) strategy is proposed via a rigorous simulation study. It is designed and implemented in a hybrid form by having the AFC loop associated with an iterative learning (IL) algorithm cascaded in series with a self-tuning fuzzy logic (FL)-based proportional-integral-derivative (PID) control for the effective overall performance of the proposed ABS. Both the IL and FL techniques are for the appropriate acquisition and computation of the important parameters in the controller. From the results, it is evident that the FL-PID with ILAFC scheme shows faster and better response compared to the FL-PID and FL-PID+AFC controllers in the wake of the given load and operating conditions. The incorporation of the AFCbased scheme into the ABS serves to provide an enhanced and robust performance that has the potentials to be implemented in a practical and real-time system.