Abstract
Induction heating systems are characterized by model uncertainty, nonlinearity, and external disturbances, and the control accuracy of the system directly affects the performance of the heated workpiece. In order to improve the temperature control accuracy and anti-interference performance of induction heating systems, this paper proposes a composite control strategy combining fractional-order PID (FOPID) and active disturbance rejection control (ADRC). Meanwhile, for the problem of too many controller tuning parameters, an improved quantum behavior particle swarm optimization (QPSO) algorithm is used to transform the nine parameters to be tuned in fractional-order PID active disturbance rejection control (FOPID-ADRC) into a minimization value optimization problem for solving. The simulation results show that the FOPID-ADRC controller improves the anti-interference capability and control accuracy of the temperature control system, and the improved QPSO algorithm has better global search capability and local optimal adaptation value.
Highlights
Vacuum induction melting technology is a kind of induction heating technology with high heating efficiency, high speed, and low consumption
A fractional-order PID active disturbance rejection control (FOPID-ADRC) strategy is used to control the temperature in the induction heating process, and an improved quantum behavior particle swarm optimization (QPSO) is adopted to rectify the parameters of the controller in order to improve the control accuracy as well as the antidisturbance capability of the temperature control system
ADRC consists of a tracking differentiator (TD), extended state observer (ESO), and nonlinear state error feedback controller (NLSEF) [28]. e transition process v1 and the differential signals of each order of v1 of system input v can be obtained by using a tracking differentiator
Summary
Vacuum induction melting technology is a kind of induction heating technology with high heating efficiency, high speed, and low consumption. E temperature control method used in induction heating furnaces in actual industrial production processes is the classical PID control. In [5], an improved fractional-order predictive control is proposed and applied to an industrial heating furnace to improve the dynamic performance and stability of the temperature control system, but no parameter tuning method for the controller is provided. In [14], a fractional-order PID and active disturbance rejection controller (FOPID-ADRC) is applied to the speed regulation system of the nonlinear double-mass servo drive system, and it is proved that the compound controller has good robustness. A fractional-order PID active disturbance rejection control (FOPID-ADRC) strategy is used to control the temperature in the induction heating process, and an improved QPSO is adopted to rectify the parameters of the controller in order to improve the control accuracy as well as the antidisturbance capability of the temperature control system.
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