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ISRN Artificial Intelligence
Volume 2013 (2013), Article ID 543607, 11 pages
http://dx.doi.org/10.1155/2013/543607
Research Article

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

1Department of Instrumentation Engineering, Anna University, M.I.T Campus, Chennai 600 044, India
2Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai 600 119, India

Received 31 March 2013; Accepted 27 April 2013

Academic Editors: K. W. Chau and J. M. Molina López

Copyright © 2013 K. Latha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three ( , , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.