Title :
Improved closed loop performance and control signal using evolutionary algorithms based PID controller
Author :
Aarabi, Alireza ; Shahbazian, Mehdi ; Hadian, Mohsen
Author_Institution :
Pet. Univ. of Technol., Ahwaz, Iran
Abstract :
Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of their simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damage the system but evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, genetic algorithm (GA) and particle swarm optimization (PSO). To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.
Keywords :
closed loop systems; evolutionary computation; genetic algorithms; particle swarm optimisation; step response; three-term control; DC motor plant; GA tuning methods; IAE; PID parameters; PSO tuning methods; Ziegler and Nichols tuning method; amplitude factors; closed loop performance; closed loop system; control signal; evolutionary algorithms based PID controller; genetic algorithm; integral absolute error; maximum overshoot; particle swarm optimization; proportional-integral-derivative controllers; quality factors; step response; Cost function; Genetic algorithms; Particle swarm optimization; Q-factor; Tuning; PID controller; evolutionary algorithm; genetic algorithm; particle swarm optimization;
Conference_Titel :
Carpathian Control Conference (ICCC), 2015 16th International
Conference_Location :
Szilvasvarad
Print_ISBN :
978-1-4799-7369-9
DOI :
10.1109/CarpathianCC.2015.7145034