Iterative learning control of molten steel level in a continuous casting process

In this paper, an iterative learning control (ILC) method is introduced to control molten steel level in a continuous casting process, in the presence of disturbance, noise and initial errors. The general ILC method was originally developed for processes that perform tasks repetitively but it can also be applied to periodic time-domain signals. To propose a more realistic algorithm, an ILC algorithm that consists of a P-type learning rule with a forgetting factor and a switching mechanism is introduced. Then it is proved that the input signal error, the state error and the output error are ultimately bounded in the presence of model uncertainties, periodic bulging disturbances, measurement noises and initial state errors. Computer simulation and experimental results establish the validity of the proposed control method.

Research highlights
► An iterative learning controller (ILC) is introduced to control a molten steel level system.
► To propose a more realistic algorithm, an P-type ILC with a forgetting factor and a switching mechanism is introduced.
► It is proved that errors are ultimately bounded under periodic bulging disturbances.
► Simulation and experimental results establish the validity of the proposed control method.