Robust iterative learning control for alloy batch weighing process in refining furnace

This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional (2D) weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing process. Third, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality (LMI) obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.