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

Abstract

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

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

“…Compared with the intelligent algorithms, 17) mechanism modeling can accurately and intuitively describe the change process of the modeled object. However, the production process of the strip is accompanied by complex procedures which have a large time delay and a strongly coupled 4 nonlinearity.…”

“…(1) Concerning the selection problem of traditional RBFNN modeling parameters 17,19) , this paper uses an IQPSO algorithm to optimize the parameters of the RBFNN, which enhances efficiency.…”

Multi-Zone Integrated Iterative-Decoupling Control of Temperature Field of Large-Scale Vertical Quenching Furnaces Based on ESRNN

“…The control of the temperature field in the furnace has the distributed parameter characteristic. Model-based temperature control for thermal processing systems has been studied [14][15][16][17]. By constraining the heat fluxes to a piecewise linear function, a discretetime state-space system is obtained.…”