Model Reference Adaptive Control Based on GANN 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 study, a model reference adaptive control using the Neural Network with Genetic Algorithm (GANN) for the temperature control of the vertical electric furnace is proposed. The neural model of the system is identified by the genetic algorithm. Another neural network is trained to learn the inverse dynamics of the system 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 show that the model is satisfied and the control is effective.