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Control of chaotic systems using fuzzy clustering identification and sliding mode control

Salarieh, H ; Sharif University of Technology | 2004

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  1. Type of Document: Article
  2. DOI: 10.1142/9789812702661_0112
  3. Publisher: World Scientific Publishing Co. Pte Ltd , 2004
  4. Abstract:
  5. In this paper we use a combination of fuzzy clustering estimation and sliding mode method to control a chaotic system, which its mathematical model is unknown. At first the model of chaotic system is identified without using any input noise signal. In this case the recurrent property of chaotic behavior is used for estimating its model. After estimating the fuzzy model of chaos, control and on-line identification of the input-related section is applied. In this step, we estimate the system model in normal form, such that the dynamic equations can be used in sliding mode control. Finally the proposed technique is applied to the Lorenz system as an example of chaotic system. The simulation results verify the effectiveness of this approach in controlling an unknown chaotic system
  6. Keywords:
  7. Algorithms ; Spurious signal noise ; Sliding mode control ; Nonlinear equations ; Neural networks ; Mathematical models ; Linearization ; Feedback control ; Fuzzy sets ; Control systems
  8. Source: Applied Computational Intelligence - Proceedings of the 6th International FLINS Conference, Blankenberge, 1 September 2004 through 3 September 2004 ; 2004 , Pages 623-628 ; 9812388737 (ISBN); 9789812388735 (ISBN)
  9. URL: https://www.worldscientific.com/doi/10.1142/9789812702661_0112