Advances in control of inductive heating by introducing model based control techniques

被引:0
|
作者
Ritt, HM [1 ]
Rake, H [1 ]
Sebus, R [1 ]
Henneberger, G [1 ]
机构
[1] Inst Automat Control, Aachen, Germany
关键词
D O I
暂无
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
An important step in processing of SSM is the inductive reheating of the raw material. Using conventional technologies the process behavior is unsatisfactory in terms of reproducibility and disturbance rejection. But reproducibility is a hard requirement for quality control in the forming process that follows the reheating in the commonly used SSM process flow. One reason for this problem is the lack of consideration of disturbances in todays control structures where often open loop systems are used. Therefore in this paper a modern control structure is presented, that uses a model of the system to calculate control inputs. First, in this article we describe the derivation of a finite element method (FEM) model of the inductive heating process. For validation the results of simulations with this model are shown in comparison to real plant data. This model is too complex for online calculations and has to be simplified for use in a controller application. Based on this simplified model a model based controller is introduced. The material properties needed for this model can be derived by parameter identification. The article focuses especially on the question of applicability of advanced control designs in a production environment where sensor data, necessary for these controllers are difficult to obtain. Therefore the derived simplified model will be used to estimate the temperature field during reheating based on only little online data. The precision of this estimation will be discussed by comparing the results with real plant data. For optimizing the SSM process a method for adjusting the reheating time is shown which is able to adapt the reheating process to a varying process flow. The closed loop performance of the controller is discussed.
引用
收藏
页码:669 / 676
页数:8
相关论文
共 50 条
  • [41] Introducing System Identification Strategy into Model Predictive Control
    WANG Jianhong
    RICARDO A.Ramirez-Mendoza
    JORGE de J Lozoya Santos
    JournalofSystemsScience&Complexity, 2020, 33 (05) : 1402 - 1421
  • [42] Recent Advances in Model Predictive and Sliding Mode Current Control Techniques of Multiphase Induction Machines
    Rodas, Jorge
    Gonzalez-Prieto, Ignacio
    Kali, Yassine
    Saad, Maarouf
    Doval-Gandoy, Jesus
    FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [43] Temperature Control of a Commercial Building With Model Predictive Control Techniques
    Mantovani, Giancarlo
    Ferrarini, Luca
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (04) : 2651 - 2660
  • [44] Simulation Model of Cascade Control of the Heating System
    Tothova, Maria
    Balara, Milan
    Dubjak, Jan
    INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA, 2015, 18 : 20 - 27
  • [45] A Review of Heating and Temperature Control in Microfluidic Systems: Techniques and Applications
    Miralles, Vincent
    Huerre, Axel
    Malloggi, Florent
    Jullien, Marie-Caroline
    DIAGNOSTICS, 2013, 3 (01) : 33 - 67
  • [46] DEVELOPMENT OF NEW CONTROL TECHNIQUES FOR THE VENTILATION AND HEATING OF LIVESTOCK BUILDINGS
    BERCKMANS, D
    GOEDSEELS, V
    JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 1986, 33 (01): : 1 - 12
  • [47] Scenario-based nonlinear model predictive control for building heating systems
    Pippia, Tomas
    Lago, Jesus
    De Coninck, Roel
    De Schutter, Bart
    ENERGY AND BUILDINGS, 2021, 247 (247)
  • [48] Model-based control of strip temperature for the heating furnace in continuous annealing
    Yoshitani, N
    Hasegawa, A
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 1998, 6 (02) : 146 - 156
  • [49] A thermal control methodology based on a machine learning forecasting model for indoor heating
    Abdellatif, Makram
    Chamoin, Julien
    Nianga, Jean-Marie
    Defer, Didier
    ENERGY AND BUILDINGS, 2022, 255
  • [50] Output voltage control of inductive power transfer system based on extremum seeking control
    Yuan, Xiaofang
    Zhang, Yunling
    Wang, Yan
    Li, Zhongqi
    IET POWER ELECTRONICS, 2015, 8 (11) : 2290 - 2298