Artificial intelligent-based feedforward optimized PID wheel slip controller

Abstract

Continual improvement of the anti-lock braking system control strategy is the focus of this work. Advances in auto-electronics and sub-systems such as the brake-by-wire technology are the driving forces behind the improvement of the anti-lock braking system. The control strategy has shifted from speed-control to slip-control strategy. In the current slip-control approach, proportional-integral-derivative (PID) controller and its variants: P, PI and PD have been proposed in place of the bangbang controller mostly used in commercial ABS. Though the PID controller is famous due to its wide applications in industry: irrespective of the nature of the process or system, it might lead to limited performance when applied to the ABS. In order to improve the performance of the PID controller, a neural network inverse model of the plant is used to optimize the reference input slip. The resultant neural network-based PID ABS is then tested in Matlab R /Simulink R simulation environment. The results of the proposed controller, exhibits more accurate slip tracking than the PID-slip controller.

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