Adaptive radial basis function sliding-mode controller for the molten steel level control of strip casting processes

Since the twin-roll strip casting process has the properties of nonlinear uncertainty and time-varying characteristics, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive radial basis function sliding-mode controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. It has on-line learning ability for responding to the system's nonlinear and time-varying behaviors. Since this model-free controller has simple control structure and small number of control parameters, it is easy to implement. Simulation results based on semi-experimental system dynamic model and parameters are executed to show the control performance of the proposed intelligent controller.