Design of Genetic Algorithm Based Fractional Order Controller for Temperature Control of Electric Furnace for Glass Tempering Process

Abstract

Safety glass or Tempered glass is made through the process by chemical action or thermal method to increase its strength compared through the normal or annealed glass. Therefore, in the thermal tempering industrial process, the temperature control of the electric furnace is the main problem. This thesis is proposed as a Genetic Algorithm based Fractional Order method designed to control the temperature of the electric furnace for the tempering process of glass. The simulation results show that the Genetic Algorithm-based Fractional Order controller can provide improved closed-loop system performances. The electric furnace is nonlinear and time-varying processes, using manual tuning PID controller is difficult to meet the control requirement. Because it is not robust, has not good dynamic response, and has a large percentage overshoot. Hence Genetic Algorithm based Fractional Order PID (GA-FOPID) is used to improve and optimize the system response, achieve steady-state and transient requirements. Therefore, in this thesis, a model of the glass tempering electric furnace is prepared using MATLAB/Simulink, and then FOPID controller with optimizing algorithm GA has been designed and simulated and its performance has been compared with Model Reference Adaptive Control (MRAC), and parameter variations of each controller. The performance specifications of each controller are compared. It is observed that from the system response and performance specifications, the GA-based FOPID controller is used to improved and optimized the system output and transient requirements to control the temperature of the electric furnace for the tempering process of glass.