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ArticleName Investigation into the evolution of identification of metallurgical process mathematical models when creating real automatic control systems
DOI 10.17580/tsm.2016.11.11
ArticleAuthor Salikhov Z. G., Ginsberg K. S.

V. A. Trapeznikov Institute of Control Sciences (Russian Academy of Sciences), Moscow, Russia:

Z. G. Salikhov, Honoured Science Worker of the Russian Federation, Professor, Chief Researcher of the Laboratory of Control Systems Identification, e-mail:
K. S. Ginsberg, Senior Researcher of Laboratory of Control Systems Identification


General concepts are given of the identification of mathematical models for multivariable automated process forecasting, training and control systems. Identification is considered as a systematic approach to the object (system) and the human subject as an important component of core activities for the creation of a real system with the mentioned features. General concepts of the relationship between the subject (identifier) and object (system) help to create an automation methodological basis for the identification process itself. By elaborating on these concepts and making them more specific, one may come to a full understanding of the cognitive activity behavioral pattern and importance of identification subject at the pre-design stage and in the course of creation of real multifunctional systems. However, complex issues happen to take place during the mathematical support development for a behaviour pattern. The issue, relating to the mathematical description of this relationship and the objective of quantitative assessment of the influence of cognitive abilities and knowledge of the subject on the process of achieving the required quality parameters of systems, have not been sufficiently studied yet. In fact, currently any scientific and technical publication starts with the development of a mathematical model for the phenomena are investigated. However, identification matters have not been already fully resolved on the basis of scientifically proven regularities and their quantitative assessments that meet the equation conditions at all intervals of model and object state coordinates variation. The biggest problem of identification of complex system mathematical models (in particular of control systems for nonferrous metallurgy processes) sill remains the lack of behaviour pattern of cognitive activity of the identification subject at pre-design stage and during the implementation of the working design of real automatic control system for complex facility. In the context of scientific and technical issues described in the article, the idea of necessary detailed scientific and engineering investigation into the relationship of human factors with the processes of improvement, identification and design of complex automated systems becomes clearly visible. The main characteristics of this relationship are given on the basis of theory and experience which were accumulated in the laboratory No. 41 at the V. A. Trapeznikov Institute of Control Sciences and JSC “Soyuztsvetmetavtomatika”.

keywords Metallurgical processes, creation of real automatic control system, pre-design stage works and design stages, structural identification, identification of mathematical models of complex systems, organization of identification as part of the overall procedure of creating a real automatic control system, identification subject, behaviour patterns of cognitive activity, search for an adequate object model

1. Arunyants G. G., Rutkovskii A. L., Salikhhov Z. G., Stolbovskii D. N. Computation of Dynamic Characteristics of Control Systems: An Effectiveness Enhancement Method. Automation and Remote Control. 2005. Vol. 66, No. 4. pp. 562–569.
2. Rotach V. Ya. Teoriya avtomaticheskogo upravleniya (Automatic control theory). Moscow : Publishing House of Moscow Energetic Institute, 2008. 396 p.
3. Emelyanov S. V., Korovin S. K., Rykov A. S., Myshlyaev L. P., Lvova E. I., Ivushkin A. A., Kazakova L. G. Metody identifikatsii promyshlennykh obektov v sistemakh (Methods of identification of industrial objects in systems). Kemerovo : Kuzbassvuzizdat, 2007. 307 p.
4. Markov Yu. G. Funktsionalnyy podkhod v sovremennom nauchnom poznanii (Functional approach in modern scientific knowledge). Novosibirsk : Nauka, 1982. 266 p.
5. Pillonetto G., Dinuzzo F., Chen T., De Nicolao G., Ljung L. Kernel methods in system identification, machine learning and function estimation: а survey. Automatica. 2014. Vol. 50, No. 3. pp. 657–682.
6. Salikhov Z. G. Matematicheskoe modelirovanie protsessa flokuloobrazovaniya v rastvorakh posle neytralnogo vyshchelachivaniya v kipyashchem sloe tsinkovykh ogarkov (Mathematic modelling of the process of floccule-formation in solutions after the neutral leachiing in the boiling layler of zinc cinders). Izvestiya vuzov. Tsvetnaya metallurgiya = Universities’ Proceedings. Non-ferrous Metallurgy. 1981. No. 6. pp. 26–32.
7. Aizerman M. A. A Man and a Collective as Elements of a Control System. Automation and Remote Control. 1975. Vol. 36, No. 5, P. 1. pp. 776–785.
8. Isermann R. New result on the identification of processes. Automatica. 1971. Vol. 7, No. 2. pp. 191–197.
9. Salikhov Z. G., Arunyants G. G., Rutkovskiy A. L. Sistemy optimalnogo upravleniya slozhnymi obektami (Systems of optimal control of complex objects). Moscow : Teploenergetik, 2004. 496 p.
10. Ik Houane F., Giri F. A Unified Approach for the Identification of SISO/MIMO Wiener and Hammerstein Systems. IFAC Proceedings Volumes. 2012. Vol. 45, No. 16. pp. 2–6.
11. Carvajal R., Delgado R., Juan C. Agüero J. C., Goodwin G. C. An identification method for Errors-in-Variables systems using incomplete data. IFAC Proceedings Volumes. 2012. Vol. 45, No. 16. pp. 1359–1364.
12. Lennart Ljung. Identifikatsiya sistem. Teoriya dlya polzovatelya (System Identification: Theory for the User). Translated from English. Moscow : Nauka, 1991. 432 p.
13. Markovsky I. An application of system identification in metrology. Control Engineering Practice. 2015. Vol. 43. pp. 85–93.
14. Anisimov S. A., Dynkin V. N., Kasavin A. D., Lototskiy V. A., Raybman N. S., Chadeev V. M. Osnovy upravleniya tekhnologicheskimi protsessami (Basis of technological process control). Under the editorship of N. S. Raybman. Moscow : Nauka, 1978. 440 p.
15. Bunich A. L., Bakhtadze N. N. Sintez i primenenie diskretnykh sistem upravleniya s identifikatorom (Synthesis and application of discrete control systems with identifier). Moscow : Nauka, 2003. 232 p.
16. Pieter Eykhoff. Osnovy identifikatsii sistem upravleniya (System Identification: Parameter and State Estimation) Translated from English. Moscow : Mir, 1975. 678 p.
17. Stoev J., Schoukens J. Nonlinear system identification — application for industrial hydro-static drive-line. Control Engineering Practice. 2016. Vol. 54. pp. 154–165.
18. Ginsberg K. S. System Laws and Identification Theory. Automation and Remote Control. 2002. Vol. 63, No. 5. pp. 838–849.
19. Prangishvili I. V., Lototskiy V. A., Ginsberg K. S., Smolyaninov V. V. Identifikatsiya sistem i zadachi upravleniya: na puti k sovremennym sistemnym metodologiyam (System identification and control problems: on the way to modern system methodologies). Problemy upravleniya = Control sciences. 2004. No. 4. pp. 2–15.
20. Salikhov Z. G. Ispolzovanie kognitivnogo metoda pri sozdanii avtomatizirovannykh piro-gidrometallurgicheskikh protsessov (Using the cognitive method during the creation of automated pyro-hydrometallurgical processes). Tsvetnye Metally = Non-ferrous metals. 1998. No. 10/11. pp. 35–44.

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