Adaptive Control using AI for the Induction Motors which Drive the Hearth of an Industrial Furnace

The aim of this paper is to present some adaptive control strategies to improve the operation of the induction motors that are responsible for the driving of a rotary heart furnace. The proposed control structures use PI and PID controllers, which are further discretized and after that used with variable sampling time. The applied adaptive strategy of varying the sampling time has the main purpose to improve the control system performances. The adaptive control strategy for the PI and PID controllers is developed by using artificial intelligence techniques based on the implementation of neural networks. The neural networks generate the controllers' coefficients which are functions depending on the sampling time. The simulations results prove the fact that the controllers are able to track with high performances different speed values, even if the induction motors behavior is a strong nonlinear one.