Intelligent robust control of unstable dynamic object with remote knowledge base tuning

Intelligent robust control of unstable dynamic object with remote knowledge base tuning

Andrey G. Reshetnikov
PhD in Technical Sciences, Joint Institute for Nuclear Research (JINR), Meshcheryakov Laboratory of Information Technologies, Senior Research Scientist, 6, ul. Joliot-Curie, Dubna, Moscow region, 141980, Russia; Dubna State University, Institute of System Analysis and Control, Assistant Professor, 19, Universitetskaya ul., Dubna, Moscow region, 141980, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0003-2528-5201

Olga Yu. Tyatyushkina
PhD in Technical Sciences, Dubna State University, Institute of System Analysis and Control, Assistant Professor, 19, Universitetskaya ul., Dubna, Moscow region, 141980, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0009-0004-2343-0799

Sergey V. Ulyanov
Doctor of Physical and Mathematical Sciences, Joint Institute for Nuclear Research (JINR), Meshcheryakov Laboratory of Information Technologies, Chief Research Scientist, 6, ul. Joliot-Curie, Dubna, Moscow region, 141980, Russia; Dubna State University, Institute of System Analysis and Control, Professor, 19, Universitetskaya ul., Dubna, Moscow region, 141980, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-7409-9531

Received November 25, 2023

Abstract
This work is a continuation of series of papers published in previous issues of the electronic journal. The control object is a classical problem of the theory of control «Cart–Pole». In the experiment used a more sophisticated robot with second feedback. In this paper the technology of fuzzy controller design based on the measured physical signal for creating teaching signal is described. In this paper the comparison of the fuzzy controllers is described using the software tool as «Soft Computing Optimizer» with the classical PID controller. Setting up a knowledge base of fuzzy controller is carried out using a remote connection to the control object in real time.

Key words
Intelligent control, fuzzy controller, genetic algorithm, neural network, knowledge base, remote tuning.

DOI
10.31776/RTCJ.12405

Bibliographic description
Reshetnikov, A.G., Tyatyushkina, O.Yu. and Ulyanov, S.V. (2024), "Intelligent robust control of unstable dynamic object with remote knowledge base tuning", Robotics and Technical Cybernetics, vol. 12, no. 4, pp. 280-287, DOI: 10.31776/RTCJ.12405. (in Russian).

UDC identifier
681.5:004.032.26:007.52

References

  1. Ulyanov, S.V. and Litvinceva L.V. (2011), Intellektual'noe robastnoe upravlenie: tekhnologii myagkih vychislenij [Intelligent robust control: soft computing technologies], VNIIGeosystem, Moscow, Russia. (in Russian).
  2. Reshetnikov, A.G. and Ulyanov, S.V. (2013), “Method of knowledge extraction from physical teaching signal: design knowlegde base for fuzzy controllers”, System analysis in science and education, 211-248, available at: https://sanse.ru/index.php/sanse/article/view/170 (Accessed 20 November, 2023). (in Russian).
  3. Ulyanov, S.V., Reshetnikov, A.G. and Kerimov, T.A. (2013), “Remote design technology of robust intelligent control at autonomous robot. Pt. 1: knowlenge base optimizer based on soft computing”, System analysis in science and education, 285-313, available at: https://sanse.ru/index.php/sanse/article/view/173 (Accessed 20 November, 2023). (in Russian).
  4. Chun-E, Li Dong-Hai, SU (2011), “Yong,Simulation and Robustness Studies on an Inverted Pendulum”, HUANG Proceedings of the 30th Chinese Control Conference, July 22-24, 2011.Yantai, China, pр. 615-619.