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Application of competitive and transition petri layers in adaptive neuro-fuzzy controller

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Języki publikacji
EN
Abstrakty
EN
The article is a summary of previous work on the possibility of using Petri layers in adaptive neuro-fuzzy controllers. In the first part of the paper the controller and two types of Petri layer have been presented, competitive layer which resets certain signals and transition layer which causes omission of signals. Layer properties were described and comparison has been made. In the second part of the paper, the results of a simulation showing the advantages and disadvantages of proposed solutions have been presented. Both quality of reference signal tracking and energetic cost of control process have been calculated. In the last part, analysis and comments on the results were made. Main conclusions are that transition Petri layer can significantly reduce growth of numerical cost of the algorithm despite the increase of fuzzy rules count. Also both competitive Petri layer and transition Petri layer by changing some inner signals can affect output value of the fuzzy system and thus the control quality indicators change. Most positive solutions have been pointed out
Słowa kluczowe
Wydawca
Rocznik
Strony
103--115
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
  • Wroclaw University of Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
  • [1]CHEN Y., TENG CH., A model reference control structure using a fuzzy neural network, Fuzzy Sets and Systems, 1995, Vol. 73, No. 3, 291–312.
  • [2]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, 2015, Vol. 34, No. 3, 792–807.
  • [3]DERUGO P., SZABAT K., A novel implementation algorithm for a fuzzy controller based on the matrix form of the controller, Przegląd Elektrotechniczny, 2014, Vol. 90, No. 11, 235–238, (in Polish).
  • [4]DERUGO P., SZABAT K., Implementation of the low computational cost fuzzy PID controller for two-mass drive system, 16th Int. IEEE Conf. and Exposition Power Electronics and Motion Control PEMC, 2014, 564–568.
  • [5]DERUGO P., DYBKOWSKI M., SZABAT K., Analysis of the impact of the position of competitive Petri layer in neuro-fuzzy adaptive controller on the dynamic properties of the drive system, Przegląd Elektrotechniczny, 2014, Vol. 90, No. 6, 35–39 (in Polish).
  • [6]DERUGO P., Analysis of competitive Petri layers impact on fuzzy Mamdani type regulator performance, Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of the Wrocław University of Technology. Studies and Research, 2013, Vol. 33, 97–110, (in Polish).
  • [7]DERUGO P., DYBKOWSKI M., SZABAT K., Application of the adaptive neuro-fuzzy speed controller with Petri layers to electrical drives, Przegląd Elektrotechniczny, 2013, Vol. 89, 64–67.
  • [8]JUANG C.F., HSU C.H., Temperature Control by Chip-Implemented Adaptive Recurrent Fuzzy Controller Designed by Evolutionary Algorithm, IEEE Trans. Circuits and Systems, 2005, Vol. 52, No. 11, 2376–2384.
  • [9]KABZIŃSKI J., KACERKA J., TSK Fuzzy Modeling with Nonlinear Consequences, Artificial Intelligence Applications and Innovations, 2014, 498–507.
  • [10]KLUSKA J., HAJDUK Z., Digital Implementation of Fuzzy Petri Net Based on Asynchronous Fuzzy RS Flip-Flop, Proc. 7th Int. Conf., Zakopane, Poland, 2004.
  • [11]KNYCHAS S., SZABAT K., Adaptive Recurrent Neuro-Fuzzy Control of the Complex Drive System, EuroCon’2013, Zagreb, Croatia, 2013, 1932–1936.
  • [12]LU C.-H., TSAI C.-C., Generalized predictive control using recurrent fuzzy neural networks for industrial processes, Journal of Process Control, 2007, Vol. 17, No. 1, 83–92.
  • [13]LIN F.J., WAI R.J., LEE C.C., Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC-DC converter, IEEE Proc. Control Theory and Applications, 1999, Vol. 146, No. 1, 99–107.
  • [14]ORŁOWSKA-KOWALSKA T, SZABAT K., Control of the drive system with stiff and elastic couplings using adaptive neuro-fuzzy approach, IEEE Trans. Industrial Electronics, 2007, Vol. 54, No.1, 228–240.
  • [15]WAI R., LIU C.H., Design of Dynamic Petri Recurrent Fuzzy Neural Network and Its Application to Path-Tracking Control of Nonholonomic Mobile Robot, IEEE Trans. Industrial Electronics, 2009, Vol. 56, No. 7, 2667–2683.
  • [16]WAI R., LIU C.H., Experimental Verification of Dynamic Petri Recurrent-Fuzzy-Neural-Network Path Tracking Control for Mobile Robot, Int. Conf. on Control and Automation, 2009, 1359–1364.
  • [17]WAI R., CHU C.H., Motion Control of Linear Induction Motor via Petri Fuzzy Neural Network, IEEE Trans. Industrial Electronics, 2007, Vol. 54, No. 1, 281–295.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-062abbc0-635c-443d-a3d2-c8f2afe0f863
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