Adaptive neural event-triggered design for the molten steel level in a strip casting process

Abstract

This paper considers an adaptive neural event-triggered control problem of the molten steel level for twin roll strip casting systems with the inclined angle. Firstly, the model is improved and simplified into an affine nonlinear system by exploiting input compensation technique. Then, an adaptive observer is established based on radial basis function neural network (RBFNN). Furthermore, an adaptive event-triggered control scheme and corresponding adaptive updated laws are designed. It is proved that the proposed control method can guarantee the system output can follow reference signal and all closed-loop signals are bounded. Finally, the validity of the control scheme is verified through semi-experimental system dynamic model.

Keywords

• casting process
• strip
• mathematic model
• molten steel level control
• radial basis function neural network (RBFNN)