Application of the Consistency Principle of Identification and Control Subsystems in Adaptive Systems
DOI:
https://doi.org/10.18372/1990-5548.75.17556Keywords:
identification, adaptation, goal orientation, optimization, functional reliability, automatic controlAbstract
The methodology of creating functionally reliable adaptive optimal systems of automatic control of objects, which have naturally existing characteristics of nonstationarity, nonlinearity, nonautonomy has been considered. This methodology is based on the principle of consistency of identification and control systems in the task of designing an adaptive control system when filtering is focused on optimality of identification, identification – on optimal control and control on the main indicator of optimality of the system. When the apriori information is limited, the global extremum is achieved on the basis of a multi-step relaxation process of optimization of each of the filtering, identification and control subsystems based on the accumulation of a posteriori information. Functional reliability and optimality is achieved by building an object identification subsystem focused on the management quality indicator. The workability of the considered principle was verified when solving the problem of optimal adaptive control of the roll channel of the missile and the problem of adaptive speed-optimal control along the real object roll channel by an optimally stabilized system.
References
F. Golnaraghi and B. Kuo, Automatic Control Systems: Tenth Edition. McGraw-Hill Education, 2017, 800 p.
M. Ya. Ostroverkhov, A. M. Silvestrov, and O. M. Skrynnyk, Systems and methods of identification of electrotechnical objects: monograph. Kyiv: NAU, 2016, 323 p. [in Ukranian]
V. V. Samsonov, А. M. Silvestrov, and L. Yu. Spinul, Multiple adaptive system of identification: monograph. Kyiv: National University of Food Technologies, 2018, 229 p.
A. Özdemir aand M. Turkoz, “Development of a D-optimal design-based 0–1 mixed-integer nonlinear robust parameter design optimization model for finding optimum,” Computers & Industrial Engineering, vol. 149, 2020. https://doi.org/10.1016/j.cie.2020.106742
A. Zelner, “Bayesian analysis in econometrics,” Journal of Econometrics, vol. 37, Issue 1, 1988, pp. 27–50. https://doi.org/10.1016/0304-4076(88)90072-3
V. Dubovoi, R. Kvietnyi, O. Mykhalov, and A. Usov, Modeling and optimization of systems: a textbook. Vinnitsa: Edelweiss, 2017, 804 p. [in Ukranian]
M. Ya. Ostroverkhov, A. M. Silvestrov, and G. I. Kryvoboka, Identification of electrotechnical control objects: monograph. Kyiv: National Technical University of Ukraine "Kyiv Politechnic Institute", 2020, 353 p. [in Ukranian]
N. V. Kadam, “Practical Design of Flight Control Systems: Some Problems and their Solutions,” Defence Science Journal, vol. 55, no. 3, 2005. https://doi.org/10.14429/dsj.55.1983
Levchuk, G. Manko, V. Tryshkin, and V. Korsun, Theory and practice of control objects identification: monograph. Dnipro, Ukrainian State University of Chemical Technology, 2019, 203 p. [in Ukranian]
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
