DocumentCode :
497909
Title :
Stochastic algorithm for PID tuning of bus suspension system
Author :
Karthikraja, A. ; Petchinathan, G. ; Ramesh, S.
Author_Institution :
Dept. of Instrum. & Control Eng., Arulmigu Kalasalingam Coll. of Eng., Srivilliputtur, India
fYear :
2009
fDate :
4-6 June 2009
Firstpage :
1
Lastpage :
6
Abstract :
The objective of this paper is to improve the comfortness of the bus suspension system by means of suitable stochastic algorithms such as real coded genetic algorithm (RGA) and particle swarm optimization (PSO). In this paper, active suspension of a one-quarter-bus model is considered. The disturbance in the form of displacement is taken as input to the controller. The computation was originally explained and controlled using conventional PID controllers. RGA and PSO based controller are designed and applied to this bus suspension problem in order to minimize the oscillations thereby comfortness can be achieved. The response of the system using RGA and PSO based PID controller is compared with conventional algorithms. The results show that RGA based PID controller is superior in terms overshoot and settling time. The proposed controller is used such that the system is always operating in a closed loop, which will lead to better performance characteristics.
Keywords :
closed loop systems; genetic algorithms; particle swarm optimisation; road vehicles; stochastic processes; suspensions (mechanical components); three-term control; vehicle dynamics; PID controller; PID tuning; PSO; RGA; active suspension; bus suspension system; closed loop system; displacement; one-quarter-bus model; overshoot; particle swarm optimization; real coded genetic algorithm; settling time; stochastic algorithm; Control engineering; Control systems; Genetic algorithms; Instruments; PD control; Particle swarm optimization; Pi control; Proportional control; Stochastic systems; Three-term control; Bus Suspension system; Particle Swarm Optimization (PSO); Proportional Integral Derivative controller-PID controller; Real coded Genetic Algorithm (RGA); State space model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Automation, Communication and Energy Conservation, 2009. INCACEC 2009. 2009 International Conference on
Conference_Location :
Perundurai, Tamilnadu
Print_ISBN :
978-1-4244-4789-3
Type :
conf
Filename :
5204475
Link To Document :
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