Induction Heating Furnace Temperature Control Based on the Fuzzy PID

Abstract

Induction heating furnace temperature control is a complex nonlinear hysteretic inertial process, it's difficult to obtain an accurate mathematical model because the temperature and disturb from outside is complicated. The normal PID control algorithm is hard to satisfy the standards of control. The fuzzy PID controller provided in this article is a combination between fuzzy control and the traditional PID control. The Fuzzy control theory is used to setting the ratio, the integral and the differential coefficient of the PID control. In the run-up stage, rapidity is guaranteed, overstrike and the steady-state error is up to the mustard. An example indicates that fuzzy PID control is superior to the normal PID controller.

Research on Variable Universe Fuzzy PID Control Strategy of Pipe Lining Induction Heating System

“…It is worth mentioning that, as Figure 8 shows, the steady-state error fluctuation of VUFPID control is small, and its average value is smaller than PID control, which is one of the results of the efficient use of fuzzy rules. Compared with literature [12,24], the accuracy and oscillation problems of traditional fuzzy PID control in induction heating have been better solved.…”

“…With the advance of intelligent algorithm research, advanced control methods such as genetic algorithms, neural networks, and gray fuzzy [6][7][8][9] are gradually applied to induction heating. Actually, the response speed and overshoot are optimized to a certain extent by fuzzy PID control [10][11][12][13]. However, these control strategies add an intelligent analysis system with a considerable amount of calculation before the PID controller or fuzzy PID controller directly; it means the feasibility of applying these control strategies to the lining induction heating system is questionable, because the temperature of the lining induction heating system changes quickly, and the lining powder is sensitive to temperature overshoot and accuracy.…”

An improved particle swarm fuzzy PID for adaptive control of temperature in CFRP induction heating

“…Increase the crossover probability in the later stage, which is effective out of the local optimum. In addition, the updated weight formula is as in equation ( 12), the updated learning factor formula is (13), and the modified formula of the adaptive crossover probability is equation ( 14):…”

“…Du and Luo 12 created a fuzzy PID controller for motor speed and barrel temperature control, and performed simulation comparison analysis to demonstrate that the suggested controller has better dynamic properties than traditional PID controllers for excellent and fast speed. Hou and Wang 13 applied fuzzy PID to induction heating furnace, which ensured rapidity in the start-up phase, and the overshoot and steady-state error reached critical values, its experiments demonstrates that the usual PID controller’s control effect is inferior than that of the fuzzy PID control. Chang et al 14 performed an in-depth investigation of all quantization factors, and created a self-calibration module to replicate the control capabilities of the self-calibration fuzzy logic controller using finite elements, which was then employed in induction heating.…”