A modified self-tuning fuzzy logic temperature controller for metal induction heating

Abstract

This paper presents a method to build a dynamic target curve producer corresponding to the rising time setting and the ultimate target temperature as a reference for a fuzzy logic controller that is used in the metal heating process application. To achieve this goal, there are some quantization factors in a fuzzy controller that must be set according to the system situation, as well as the experience of experts that will cause the controller to have a lack of adaptivity. To solve this problem, in this paper, all the quantization factors are analyzed thoroughly, and a self-tuning module is designed to make it possible for the controller to perform real-time adjustments based on the system situation and, eventually, make it more adaptive. During the design process, a simulation comparing the control capabilities of the conventional fuzzy logic controller and the self-tuning fuzzy logic controller (STFLC) is made using a finite element analysis. Finally, experiments are carried out on the induction heating system to verify the effect of the proposed STFLC. The results show that, with the proposed self-tuning module, the control capability and adaptivity of the controller were drastically improved.

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.…”

“…A large number of analysis calculations before an adjustment and strong oscillation near the steady state will directly affect the uniformity of the lining layer. In addition, it is worth mentioning that on the basis of fuzzy PID control, Professor Arya proposed a series of new controllers for the power system [11,[14][15][16]. Unfortunately, these controllers have not been proven to be effective in lining induction heating systems.…”

An analysis of temperature control for electromagnetic induction heating of CFRP based on sparrow search algorithm

“…Soyguder et al [13] designed an adaptive fuzzy PID controller, which adjusts the PID parameters online according to the temperature error and error change rate of the HVAC system, and achieves the minimum setting time and zero steady-state error. Chang et al [14] deeply analyzed all the quantization factors and developed a selftuning module that used finite element analysis to simulate the control ability of a selftuning fuzzy logic controller and conducted experiments on the induction heating system, verifying the effectiveness of the method. Chowdhury et al [15] proposed a fuzzy self-tuning PID controller for a preheating recovery system, tested the set point tracking and disturbance suppression ability in steady-state and transient heat cases, and found that the fuzzy self-tuning PID controller greatly reduced the calculation time and significantly improved the control performance.…”

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

“…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. Experiments are conducted on the system to validate the efficacy of the suggested technique.…”