Internal model control for electrode in electric arc furnace based on rbf neural networks

To deal with the nonlinear and time-variant of the electrode regulator system in arc furnace, an IMC controller is designed. The controller composes by two RBF neural networks, one is used to identify the controlled object and the other identifies its inverse, from this to eliminate steady-state error and to make output track input. Center vectors and the shape parameters of the networks are adjusted online, which speeds up the convergence rate and improves anti-jamming capability. Simulation results verify the effectiveness of the method.