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The use of so-called computational intelligent techniques: Genetic Algorithms (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in advanced industrial process controls is nowadays well established. The main objective of this research is to propose the use of Particle Swarm Optimization technique, which is easy to use, highly stable convergence and as well as highly computational efficiency, for tuning of PID controller which regulates the wheel slip in Antilock Braking System (ABS). The PSO searches for the control gains kp (proportional gain), ki (integral gain) and kd (derivative or differential gain) which based on the desired closed-loop control response. The control algorithm is then applied to a ¼ car ABS model. The simulations are performed by using a commercial software package of purpose to test the braking performance from the customized controllers. By comparing Ziegler-Nichols Technique and the Bang Bang-controller, the effects of shock resistance from a brake pedal pulsations and the stopping distance are minimized. Furthermore, the friction coefficient between wheels and road surface can be maintained at maximum value. This is to confirm that the PSO-based optimum designed PID controller can be effectively performed for Antilock Braking System.
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