Low-Order Model Identification and Adaptive Observer-Based Predictive Control for Strip Temperature of Heating Section in Annealing Furnace

The heating section of an annealing furnace is a plant that raises the temperature steel strips to desired target ensure achieve material properties. Model predictive control (MPC) has been used increase in previously published studies, because it reflects geometrical and characteristics strip. An accurate model for required optimization MPC. For large complex furnace, nonlinear models from existing studies are computationally expensive. Therefore, we propose identification method linear low-order model, then apply In addition, introduce parameter estimation adaptive observer address difficulties identifying unknown parameters. To verify identified observer-based MPC, results tracking shown analyzed through simulation with conditions close actual operation conditions. Furthermore, proposed compared those PI controller solves problems caused by massive computational complexity absence sensors. allows using MPC various types furnaces.