A direct adaptive controller for EAF electrode regulator system using neural networks

The electrode regulator system is a complex system with multivariable, strong coupling and strong nonlinearity, and conventional control methods such as PID cannot meet the requirements. This paper proposes an adaptive neural network controller (ANNC) for electrode regulator system. An equivalent model in affine-like form is first derived as feedback linearization methods cannot be implemented for such systems. Then, adaptive control is implemented based on the affine-like equivalent model. Pretraining is not required and the weights of the neural networks (NNs) are directly updated online based on the input–output measurement. The robustness of the stability is established by the Lyapunov method. The proposed nonlinear controller is verified by computer simulations and experiments.