Electromagnetic Induction Heating System Modeling And Control Strategy

At present, electromagnetic induction heating is an efficient way to convert the electrical energy to thermal energy. The control for electromagnetic induction heating system with high efficiency, high accuracy, stability, reliability is an important research subject. To build a suitable electromagnetic induction heating control system, it should firstly analyze the model of the electromagnetic induction system. And then can design a controller matching the characteristics of the electromagnetic induction heating system.The mechanism of the system through combining the theoretical knowledge of the circuit, the properties of electromagnetic induction and the thermal effects were studied in detail in this paper. The equivalent circuit and the Hammerstein model of the system were obtained. Through analyzing the single input-output Hammerstein model with the complexity of parameter identification, the parameters of the nonlinear module were separated, and an auxiliary model was established for the necessary data to identify the parameter of the model. Ultimately the problem of parameter identification is converted to the optimization problem of the minimum criterion function. Then the improved particle swarm optimization was applied to the function of the minimum criteria function. The simulation results show that the improved particle swarm optimization algorithm increased the particle swarm's capacity of local search and overcome the convergence and premature of the particles and got the best estimate of the parameters. For controlling the electromagnetic heating system with the Hammerstein model, an improved fuzzy adaptive PID controller including the inverse function of the nonlinear module, the Fuzzy control module and a PID control module was proposed. At the same time, it had the portability and flexibility of PID and the intelligence of Fuzzy control.The simulation on Matlab proved that the stability performance and anti-interference performance of the Fuzzy adaptive PID controller is improved, and demonstrate the controller is feasible and effective.