Optimal Design of PID Controller for a CSTR System Using BF-PSO

Abstract

Social foraging behavior of a Escherichia-colibacteria has been explored to develop a novel algorithm for distributed optimization and control. Recently hybrid approach developed, involving PSO and BFOA (bacterial foraging optimization algorithm) algorithm for optimizing multi-model and high dimensional function. This paper presents the optimal design of PID controller based on a Bacterial foraging particle swarm optimization (BF-PSO) approach for continuous stirred tank reactor (CSTR). The mathematical model of experimental system had been approximated near the operating point for the PSO algorithm to adjust PID parameters for the objective function. The results show the adjustment of PID parameters converting into the optimal point. The good control response is obtained based on the optimal values by the BF-PSO technique. Index Terms: PSO (Particle swarm optimization),BF-PSO, optimal control, simulation. I. INTRODUCTIONThe process control techniques in the industry have made great advances during the past decades. A no of control methods such as adaptive control, neural control, and fuzzy control have been studied. Among them, the best known is the proportional-integralderivative (PID) controller, which has been widely used in the industry because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of PID controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. It is hard to determine optimal or near optimal PID parameters with the classic tuning method (Ziegler-Nichol's method for instance). For these reasons, it is highly desirable to increase the capabilities of PID controllers by adding new features. Many artificial intelligence (AI) techniques have been employed to improve the controller performances for a wide range of plants while retaining their basic characteristics. AI techniques such as neural network, fuzzy system, and neural-fuzzy logic have been widely applied to proper tuning of PID controller parameters. Particle swarm optimization (PSO) [1], first introduced by Kennedy and Eberhart, is one of the modern heuristic algorithms. It was developed through simulation of a simplified social system, and has been found to be robust in solving continuous nonlinear optimization problems. The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristic than other stochastic methods. PSO method is an excellent optimization methodology and a promising approach for solving the optimal PID controller parameters. Therefore, this study develops the PSO-PID controller to search optimal PID parameters. This controller is called the PSO-PID controller. In this paper, we propose a particle swarm optimization approach for optimal design of PID controller for continuous stirred tank reactor. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity...