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Fuzzy predictive controller for chaotic flows based on continuous signals

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  • Nazarimehr, Fahimeh
  • Sheikh, Javad
  • Ahmadi, Mohammad Mahdi
  • Pham, Viet–Thanh
  • Jafari, Sajad

Abstract

In this paper, we propose a fuzzy model predictive control method, which can be used in the control of highly nonlinear and complex systems, like chaotic ones. This method only uses the obtained time series of the system and does not require any prior knowledge about the system’s equations. In our proposed method, a fuzzy model is created using a combination of Gaussian basis functions. The model is developed using initial part of the time series, sampled from an observed signal from the nonlinear chaotic system (learning phase). Then, the developed fuzzy model is used to modify the controller. The controller, which is tuned in each sample of the time series, is subsequently applied to an interval of the continuous signal and holds the system in the desired state. We investigate the efficiency of this new control method using a chaotic system with no equilibrium point, which belongs to category of chaotic systems with hidden attractor.

Suggested Citation

  • Nazarimehr, Fahimeh & Sheikh, Javad & Ahmadi, Mohammad Mahdi & Pham, Viet–Thanh & Jafari, Sajad, 2018. "Fuzzy predictive controller for chaotic flows based on continuous signals," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 349-354.
    • Handle: RePEc:eee:chsofr:v:106:y:2018:i:c:p:349-354
    DOI: 10.1016/j.chaos.2017.12.005
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    References listed on IDEAS

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    1. Jafari, Sajad & Sprott, J.C., 2013. "Simple chaotic flows with a line equilibrium," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 79-84.
    2. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    3. Petković, Dalibor & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lee, Malrey & Anicic, Obrad & Nikolić, Vlastimir, 2016. "Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1270-1278.
    4. Dalibor Petković & Siti Hafizah Ab Hamid & Žarko Ćojbašić & Nenad T. Pavlović, 2014. "RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
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    Cited by:

    1. Jafari, Sajad & Ahmadi, Atefeh & Panahi, Shirin & Rajagopal, Karthikeyan, 2018. "Extreme multi-stability: When imperfection changes quality," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 182-186.
    2. Jahanshahi, Hadi & Sajjadi, Samaneh Sadat & Bekiros, Stelios & Aly, Ayman A., 2021. "On the development of variable-order fractional hyperchaotic economic system with a nonlinear model predictive controller," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    3. Xinggui Li & Ruofeng Rao & Xinsong Yang, 2022. "Impulsive Stabilization on Hyper-Chaotic Financial System under Neumann Boundary," Mathematics, MDPI, vol. 10(11), pages 1-18, May.

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