Title :
Internal model control for electrode in electric arc furnace based on rbf neural networks
Author :
Yu, Feng ; Mao, Zhizhong
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
To deal with the nonlinear and time-variant of the electrode regulator system in arc furnace, an IMC controller is designed. The controller composes by two RBF neural networks, one is used to identify the controlled object and the other identifies its inverse, from this to eliminate steady-state error and to make output track input. Center vectors and the shape parameters of the networks are adjusted online, which speeds up the convergence rate and improves anti-jamming capability. Simulation results verify the effectiveness of the method.
Keywords :
arc furnaces; control system synthesis; convergence; hydraulic systems; neurocontrollers; nonlinear control systems; process control; radial basis function networks; smelting; time-varying systems; IMC controller design; RBF neural networks; antijamming capability improvement; center vectors; convergence rate; electric arc furnace; electrode regulator system; hydraulic system; internal model control; nonlinear time-varying system; output track input; shape parameters; smelting process; steady-state error elimination; Electrodes; Furnaces; Neural networks; Object recognition; Regulators; Shape; Vectors; IMC control; RBF neural network; nonlinear; time-variant;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244650