NN-Based Approximate Model Control for the EAF Electrode Regulator System

Abstract EN

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system.

According to the characteristics of electrode regulator system, an affine-like equivalent model is first derived.

Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation.

The control scheme is simple enough that it can be implemented on an automotive microcontroller system, and the performance meets the system requirements.

The stability of the system is established by the Lyapunov method.

Several simulations illustrate the effectiveness of the controller.