A PSO-based Optimum Design of PID Controller for Anti-lock Braking System

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์NKRAFA Issue12
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Published: Aug 2, 2018
Keywords:
Antilock Braking System ABS Tuning of PID Particle Swarm Optimization PSO

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Weerayuth Nantawatana
Department of Mechanical Engineering, Faculty of Engineering, Ubon Ratchathani University
Sivapong Phetsong
2Department of Mechanical Engineering , Faculty of Engineering and Industrial Technology Silpakorn University

Abstract

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.

Article Details

How to Cite
Nantawatana, W., & Phetsong, S. (2018). A PSO-based Optimum Design of PID Controller for Anti-lock Braking System. NKRAFA JOURNAL OF SCIENCE AND TECHNOLOGY, 12, 33–40. Retrieved from https://ph02.tci-thaijo.org/index.php/nkrafa-sct/article/view/155039
Issue
Vol. 12 (2016): January - December
Section
Research Articles

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