Internal model control using neural networks-genetic algorithm for vertical electric furnace

The vertical electric furnace is a multi-variable complex system, conventional control methods are used to control it, to need modelling and decoupling. In this paper, an internal model control using neural networks for the vertical electric furnace is presented. The dynamics model of the neural network of the system is identified by the genetic algorithm. Another neural network is trained to learn the inverse dynamics of the vertical electric furnace so that it can be used as a nonlinear controller. Because of the limitation of BP algorithm, the genetic algorithm is used to find the fitness weights and thresholds of the neural network model, and the simulation results testify that the model is satisfied and the control is effective.