TUNING OF PID CONTROLLER USING GENETIC ALGORITHM

Abstract

It is interesting to note that more than half of the industrial controllers in use today utilize PID or modified PID control scheme. Most PID controllers are adjusted on-site, many different type of tuning rules have been proposed in the literature. Using these tuning rules, delicate and fine tuning of PID controllers can be made on site. Also, automatic tuning methods have been developed and some of the PID controllers may possess on-line automatic tuning capabilities. Modified forms of PID control, such as I-PD control and two-degrees-of-freedom PID control, are currently in use in industry. Many practical methods for bump less switching (from manual operation to automatic operation) and gain scheduling are commercially available. Hence how do we optimize the PID controller? The usefulness of PID controls lies in their general applicability to most control systems. In particular, when the mathematical model of the plant is not known and therefore analytical design methods cannot be used, PID controls prove to be most useful. In the field of process control systems, it is well known that the basic and modified PID control schemes have proved their usefulness in providing satisfactory control, although in many given situations they may not provide optimal control. In this dissertation, it is proposed that the controller be tuned using the Genetic Algorithm technique. The Genetic Algorithm (GA) is an optimization and stochastic search technique based on the principles of genetics and natural selection. The Genetic Algorithms (GAs) are a stochastic global search method that emulates the process of natural evolution. The Genetic Algorithm starts with no knowledge of the correct solution and depends entirely on responses from its environment and evolution operators (i.e. reproduction, crossover and mutation) to arrive at the best solution. Genetic Algorithms have been shown to be capable of locating high performance areas in complex domains without experiencing the difficulties associated with high dimensionality or false optima as may occur with gradient decent techniques. Using Genetic Algorithms to perform the tuning of the controller will result in the optimum controller being evaluated for the system every time. For this study, the model selected is of turbine speed control system. The reason for this is that this model is often encountered in refineries in a form of steam turbine that uses hydraulic governor to control the speed of the turbine. The PID controller of the model will be designed using the classical method and the results analyzed. The same model will be redesigned using the Genetic Algorithm method. The results of both designs will be compared, analyzed and conclusion will be drawn out.