ADAPTIVE OPTIMAL CONTROL SYSTEM OF ORE LARGE CRUSHING PROCESS
DOI:
https://doi.org/10.15588/1607-3274-2018-1-18Keywords:
ore large crushing, control system, optimal control, identification, predictionAbstract
Context. The task of efficiency increase of power-hungry ore large crushing process by creation of optimal control system of it isdecided.
Objective is a improvement of control quality of ore large crushing process in conditions of information uncertainty about its state by
synthesis of optimal control based on identification of the process predictive model during control system functioning.
Method. It is developed the adaptive optimal control system of the ore large crushing process, which realizes the following procedures:
estimation of the controlled process state, its structural-parametric identification, prediction of the process progress, as well as synthesis
of optimal control. The solution of problem of synthesis of large crushing process optimal control is carried out during system functioning
by the principle of minimum of the generalized work on the sliding optimization interval with attraction of information about controlled
process state to the new interval of optimization and its future state by the predictive model that allows to simplify the solution of problem
of synthesis for nonlinear large crushing process and to compensate disturbances. The large crushing process identification is carried out by
definition of the operating mode and dimension of its state, based on which it is performed the model structure and parameters with the help
of composition of methods of global and local optimization that allows to increase the model accuracy.
Results. It is determined that for large crushing process the offered optimal control with prediction provides the decrease of the
control error in ~2 times and increase of productivity of the process of ore self-grinding, the next one in the technological line, (due to
stabilization of content of class +100 mm in its input ore) on 3.8%.
Conclusions. The scientific novelty of the work consists in development of adaptive system of large crushing process optimal control, in which the optimal control is formed in the course of functioning of control system by the principle of minimum of generalized work with the current estimation of the state of operated process and its future state by the predictive model that provides the control system invariance to the changes of operating modes of the equipment and the disturbing environment, and therefore, the improvement of control quality.
The practical significance of results of the work consists in development of algorithms of the current estimation and prediction of large
crushing process state, its identification and synthesis of optimal control realizing control system.
References
Maryuta A. N., Каchan Yu. G., Bunko ВV. А. Avtomaticheskoe
upravlenie technologicheskimi protsessami obogatitelnych fabrik.
Мoscow, Nedrа, 1983, 277 p.
Herbst J. A., Alba F. A., Pate W. T., Oblad A. E. Optimal Control
of Communition Operations, International Journal of Mineral
Processing, 1988, Vol. 22, No. 1–4, pp. 275–296.
Кrasovskogo А. А. pod red. Spravochnik po teorii
avtomaticheskogo upravleniya. Мoscow, Naukа, 1987, 712 p.
Кrasovskiy А. А., Bukov V. N., Shendrik V. S. Universalnye
algoritmy optimalnogo upravleniya nepreryvnymi protsessami.
Мoscow, Naukа, 1977, 272 p.
Kornienko V. I., Gusev A. Yu., Gerasina A. V., Kornienko V. I.
Methods and principles of control over the complex objects of
mining and metallurgical production, Energy Efficiency
Improvement of Geotechnical Systems. London, CRC Press,
Taylor & Francis Group, 2013, pp. 183–192.
Коrnienko V. I., Маtsyuk S. М. Optimalnoe upravlenie nelinejnym
dinamicheskim protsessom s intellektualnym prognozirovaniem
ego sostoyaniya, Shtuchnyi intellekt, 2015, No. 4, pp. 137–145.
Korniienko V. I., Маtsyuк S. М., Udovik I. М., Alekseyev A. M.
Method and algorithms of nonlinear dynamic processes
identification, Naukovyi visnyk Natsionalnogo girnychogo
universyteta, 2016, No. 1, pp. 98–103.
Ivachnenko A. G. Dolgosrochnoe prognozirovanie I upravlenie
clozhnymi sistemami. Кyiv, Теchnika, 1975, 312 p.
Nelles O. Nonlinear System Identification: From Classical
Approaches to Neural and Fuzzy Models. Berli. Springer, 2001,
p.
Кruglov V. V., Dli М. I., Golunov К. Yu. Nrchetkaya logika I
isskustvennye nejronnye seti. Мoscow, Fizmatlit, 2001, 224 p.
Shuster G. Determinirovannyi khaos. Vvedenie. Мoscow, Мir,
, 256 p.
Кuznetsov S. P. Dinamicheskiy khaos. Мoscow, Fizmatlit, 2002,
p.
Holland J. H. Adaptation in natural and artificial systems. An
introductory analysis with application to biology, control and artificial
intelligence. London, Bradford book edition, 1994, 211 p.
Voronovskiy G. К., Маkhotilo К. V., Petrashev S. N., Sеrgeev S.
N. Geneticheskie algoritmy, isskustvennye nejronnye seti i
problemy virtualnoy realnosti. Kharkov, Оsnovа, 1997, 112 p.
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Copyright (c) 2018 V. I. Korniienko, S. M. Matsiuk, I. M. Udovyk
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