Authors: Suping Cao; Xizhen Hu; Wenxia Xu; Jian Huang
Addresses: School of Automation, Huazhong University of Science and Technology, Wuhan, China ' School of Mathematics and Statistics, Wuhan University, Wuhan, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, China
Abstract: To propel the automation of production and save energy consumption, the dual adaptive control problem of the magnesium reduction furnace is studied in this paper. The detailed temperature control process is analysed and divided into four phases. The control process of one area of the furnace is described by a simplified single-in-single-out (SISO) discrete model with large input delay. With sufficient input/output experiment data, the optimal model structure and parameters are identified offline. The dual generalised minimised variance (GMV) controller which can cope with non-minimum phase plants with time delay is chosen in the temperature control of the magnesium reduction process. The control performance is verified by both simulations and experiments. The results show that the dual GMV controller is superior to a cautious controller or a conventional PID controller.
Keywords: dual adaptive control; magnesium reduction furnaces; temperature control; system identification; energy consumption; single-input single-output; SISO discrete models; input delay; generalised minimised variance; GMV controllers; simulation; modelling.
International Journal of Modelling, Identification and Control, 2016 Vol.26 No.3, pp.264 - 272
Received: 19 May 2015
Accepted: 29 Sep 2015
Published online: 08 Nov 2016 *