Experimental implementation of a vehicle anti-lock brake system employing intelligent active force control with a P-type iterative learning algorithm

Abstract

The anti-lock brake system (ABS) is an active safety device used in ground vehicles to increase the brake force between the tire and the road during panic braking. Due to the high non-linearity of the tire and road interaction plus uncertainties derived from vehicle dynamics, a standard proportional-integral-derivative (PID) controller is not deemed enough for the system to produce optimum performance. An active force control (AFC) based scheme is proposed to enhance the robustness of the system and reject undesirable disturbances. A P-type iterative learning algorithm (ILA) is implemented in the AFC loop to estimate the vital parameter continuously for force feedback compensation. In this paper, the control scheme to be known as PID-ILAFC was validated experimentally through its implementation on a test rig. A hardware-in-the-loop (HIL) test via LabVIEW was formulated with novel intelligent control schemes to execute the algorithm in real-time, thereby practically verifying the response of the ABS in the wake of parametric changes and varied operating and loading conditions. The PID-ILAFC controller is specifically designed to provide a proper slip ratio close to the reference value, a reduced stopping distance, and stability in vehicle movement during panic braking. The results clearly exhibit more robustness and superior performance of the AFC-based ABS in achieving the reduced stopping distance and good slip ratio in comparison to the PID and passive counterparts for a dry road condition setting.