IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.4, April 2008 278 Tuning Algorithms for PID Controller Using Soft Computing Techniques B.Nagaraj 1,* , S.Subha 1 ,B.Rampriya 2 , 1 Department of Electronics and Instrumentation Engg, 2 Department of Electrical and Electronics Engg Kamaraj College of engg and technology, Virudhunagar, India Summary PID controllers are widely used in industrial plants because it is simple and robust. Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. So the control engineers are on look for automatic tuning procedures. In this paper, the parameters of PID controller are tuned for controlling the armature controlled DC motor. Continuous cycling method & Z- N step response method are the conventional methods whose performance have been compared and analyzed with the intelligent tuning techniques like Genetic algorithm, Evolutionary programming and particle swarm optimization. GA, EP and PSO based tuning methods have proved their excellence in giving better results by improving the steady state characteristics and performance indices. Key words: Genetic algorithm, Evolutionary programming and particle swarm optimization 1. Introduction The general equation of PID controller is 0 p P dt de(t) Td dt e(t) Ti 1 e(t) K (t) U + × + + = ∫ Where, K p = proportional gain T i = integral time T d = derivative time The variable e(t) represents the tracking error which is the difference between the desired input value and the actual output. This error signal will be sent to the PID controller and the controller computes both the derivative and the integral of this error signal. The signal U(t) from the controller is now equal to the proportional gain (K p ) times the magnitude of the error plus the integral gain (K i ) times the integral of the error plus the derivative gain (K d ) times the derivative of the error [2]. 2. Need for controller tuning: The control system performs poor in characteristics and even it becomes unstable, if improper values of the controller tuning constants are used. So it becomes necessary to tune the controller parameters to achieve good control performance with the proper choice of tuning constants [7]. 2.1. Methods for PID Controller Tuning The PID control algorithm is used for the control of almost all loops in the process industries, and is also the basis for many advanced control algorithms and strategies. In order to use a controller, it must first be tuned to the system. This tuning synchronizes the controller with the controlled variable, thus allowing the process to be kept at its desired operating condition. Standard methods for tuning controllers and criteria for judging the loop tuning have been used for many years. Some of them are Mathematical criteria, Cohen- coon Method, Trial and error method, Continuous cycling method, Relay feed back method and Kappa-Tau tuning method. From the above mentioned methods, four have been selected, tuned, designed and the results obtained were compared. These results thus show a better method to be opted [3]. 3. Reason for Selecting Soft Computing Techniques 1. Model type: Many methods can be used only when the process model is of a certain type, for example a first order plus dead time model (FOPDT). Model reduction is necessary if the original model is too complicated. 2. Design criteria: These methods aim to optimize some design criteria that characterize the properties of the closed-loop system. Such criteria are, for example, gain and phase margins, closed-loop bandwidth, and different cost functions for step and load changes. 3. Approximations: Some approximations are often applied in order to keep the tuning rules simple. The purpose of this project is to investigate an optimal controller design using the Evolutionary programming, Genetic algorithm, Particle swarm optimization techniques.