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Reproduction of the control plane as a method of selection of settings for an adaptive fuzzy controller with Petri layer

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Języki publikacji
EN
Abstrakty
EN
The purpose of this paper is to show possibility and advantages of initial control plane reproduction for an adaptive fuzzy controller. Usually the fuzzy control is used when the object is not very well known. Yet the truth is, however, that some, at least general information about the object, is available. Usually, in such a case, optimization algorithms are used to tune the control structure. The purpose of this article is to show how to find a starting point that is closer to optimum than a statistically random point, and this way to obtain better results in a shorter time.
Rocznik
Strony
609--624
Opis fizyczny
Bibliogr. 18 poz., rys., tab., wz.
Twórcy
autor
  • Wroclaw University of Science and Technology The Department of Electrical Machines, Drives and Measurements Smoluchowskiego 19 str, 50-370 Wroclaw, Poland
  • Wroclaw University of Science and Technology The Department of Electrical Machines, Drives and Measurements Smoluchowskiego 19 str, 50-370 Wroclaw, Poland
Bibliografia
  • [1] Zadeh L.A., Fuzzy sets, Information and Control, vol. 8 no. 3, pp. 338–353 (1965).
  • [2] Demidova G.L., Lukichev D.V., Denisov K., Implementation of Type-2 Fuzzy Control of PMSM Position Drive with Flexible Coupling, International Conference on Control, Decision and Information Technologies, pp. 1917–1922 (2019).
  • [3] Chen Y.-C., Tu C.-H., Lin C.-L., Integrated electromagnetic braking/driving control of electric vehicles using fuzzy inference, IET Electric Power Applications, vol. 13, no. 7, pp. 1014–1021 (2019).
  • [4] Akcayol M.A., Application of adaptive neuro-fuzzy controller for SRM, Advances in Engineering software, vol. 35, no. 3–4, pp. 129–137 (2004).
  • [5] Civelek Z., Lüy M., Çam E., Barışçı N., Control of pitch angle of wind turbine by fuzzy PID controller, Intelligent Automation and Soft Computing, vol. 22, no. 3, pp. 463–471 (2016).
  • [6] Daode Z., Wei L., Hu X., Zhang C., Li X., Research on torque ripple suppression of brushless DC motor based on PWM modulation, Archives of Electrical Engineering, vol. 68, no. 4, pp. 843–858 (2019).
  • [7] Pradeep M., Sharmila B., Devasena D., Srinivasan K., PID and PIλDµ Controller Implementation for Speed Control Analysis of Two Mass Drive System, 2018 International Conference on Communication and Signal Processing (ICCSP) (2018).
  • [8] Demidova G.L., Lukichev D.V., Brock S., Fuzzy adaptive PID control for two-mass servo-drive system with elasticity and friction, 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) (2015).
  • [9] Derugo P., Szabat K., Implementation of the low computational cost fuzzy PID controller for twomass drive system, 16th IEEE International Power Electronics and Motion Control Conference and Exposition, pp. 564–568 (2014).
  • [10] Wai R.J., Liu C.M., Design of dynamic petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot, IEEE Transactions on Industrial Electronics, vol. 56, no. 7, pp. 2667–2683 (2009).
  • [11] Derugo P., Szabat K., Adaptive neuro-fuzzy PID controller for nonlinear drive system, COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 34, no. 3, pp. 792–807 (2015).
  • [12] Wai R.J., Chu C.C., Robust petri fuzzy-neural-network control for linear induction motor drive, IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 177–189 (2007).
  • [13] Derugo P., Application of competitive and transition petri layers in adaptive neuro-fuzzy controller, Power Electronics and Drives, vol. 1, no. 1, pp. 103–115 (2016).
  • [14] Derugo P., Szabat K., Analysis of adaptive neuro-fuzzy PD controller with competitive Petri layers in speed control system for DC motor, Computer Applications in Electrical Engineering, vol. 11, pp. 267–280 (2013).
  • [15] Li H., Wang J., Du H., Karimi H.R., Adaptive sliding mode control for Takagi–Sugeno fuzzy systems and its applications, IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 531–542 (2017).
  • [16] Derugo P., Kacerka J., Jastrz ˛ebski M., Szabat K., Linear motor control using an adaptive structure of fuzzy logic control PID, Electrical Review (in Polish), vol. 91, no. 7, pp. 93–96 (2015).
  • [17] Reda T.M., HassanY.K., Elarabawy I.F., Abdelhamid T.H., Comparison Between Optimization of PI Parameters for Speed Controller of PMSM by Using Particle Swarm and Cuttlefish Optimization, IEEE Twentieth International Middle East Power Systems Conference, pp. 986–991 (2018).
  • [18] Kabziński J., Kacerka J., Optimization of Polytopic System Eigenvalues by Swarm of Particles, International Conference on Artificial Intelligence: Methodology, Systems, and Applications, pp. 178–185 (2014).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4b4c44cc-f043-4291-a708-62b6977c29f1
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