Pid Control Parameters Optimization Based On Genetic Algorithm In The Application Of The Temperature Monitoring System

PID control is the most common control method used in process control especially broad used in industrial process control system because of its single algorithm, good robustness and high reliability. The performance of PID parameters determines whether industrial equipments run efficiently and stably. In the past industrial production process, the parameters’tuning of the PID controller mainly depended on the manual mode. This approach requires not only specialized technical personel, and it is more time-consuming. With the development of industrial economy, the parameter setting gradually put on the agenda, this paper is to design a fast and reliable parameters’tuning method of the PID controller.Genetic algorithm is a new optimizing searching mechod based on bilogy evolutionary theory. The basic principle of genetic algorithm and its basic operation process are firstly studied. Then the disadvantages of the basic genetic algorithm are analyzed. For example, it is easy to premature in the early times and has a slow evolution speed in the later times. Balance the length of the encoding of the basic genetic algorithm and parameter accuracy using the adaptive genetic algorithm. Parameters’optimizing of the PID controller in second order systems, delay systems and nonlinear systems using basic genetic algorithm and adaptive genetic algorithm. So the results prove that the feasibility and availability of genetic algorithm in the optimizing of PID controllers’ parameters. Comparision result shows that the adaptive genetic algorithm has good convergence.Establish the mathematical model of the furnace temperature field and describe the application of specific projects furnace temperature control system by researching the heat transfer principle and the basic principles of the temperature field. Based on simulation, this paper will research the parameters’tuning of the PID controller application in furnace temperature system. Simulation and analysis the tracking, noise immunity and robustness according the system, demonstrate that their better control precision and dynamic characteristics.