Title :
The Iterative Learning Control System for 600MW Unit Milling System
Author :
Liu Keke ; Xie Youcheng
Author_Institution :
Changsha Univ. of Sci. & Technol., Changsha, China
Abstract :
The ball mill pulverizing system with multi-variable, strong coupling, large inertia and time-varying etc characteristics, long-term since difficult to input automatic control, therefore become electric power industry automatic control research hot spot problems. This paper studied and analyzed in detail the working principle of the ball mill pulverizing system and work characteristics, using multiple model objects description its dynamic characteristics, based on iterative learning control, controller design and simulation for the ball mill system. The results show that the ball mill system variability, coupling and delayed nature can be well solved by the iterative learning control, achieved good control effect.
Keywords :
ball milling; control system synthesis; iterative methods; learning systems; multivariable control systems; power plants; time-varying systems; automatic control; ball mill pulverizing system; ball mill system variability; controller design; dynamic characteristics; electric power industry; iterative learning control; multiple model object description; multivariable strong coupling; power 600 MW; time varying characteristics; Coal; Control systems; Couplings; Milling; Powders; Process control; Time varying systems; Iterative learning control; Mathematical model; Multivariable systems; System powder system;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
DOI :
10.1109/ICDMA.2011.318
