International Journal of Engineering and Technology

PID controller is mostly used in process plants to control the system performance by properly choosing its parameters. The optimum PID parameters can be obtained in offline using genetic algorithm if the mathematical model of the system is exactly known. In all process plants the process parameters such as properties of materials like thermal conductivity, electrical conductivity, physical dimensions such as diameter, length of the pipes, parameters of valves and pumps will change as time runs. This happens due to corrosion, scaling, aging, repairs during the maintenance, wear and tear. When the system is robust these changes slightly affect the performance of the system. When the system is not robust they make the system performance worst. Due to above reasons the process plant parameters changes as time runs. It is not easy to measure the changes in system parameters while plant is running and could not be evaluated optimum PID parameters through mathematical model. In this paper a new approach using genetic algorithm and neural network is established for optimum self tuning of PID parameters by observing the time response of the system at any time while plant is running.