Identification of nonlinear systems and its application to Model Predictive Control

• Autorzy: Kashiwagi H. , Harada H.
• Języki publikacji: EN

Abstrakty

EN

This paper describes a new method for identifying and separating Volterra kernels of nonlinear control systems by use of pseudorandom M-sequence and correlation technique, and its application to model predictive control. By use this identification method, we can obtainVolterra kernels of up to 3rd order of a nonlinear system. M-sequence is applied to a nonlinear system and the crosscorrelation function between the input and output is calculated. then the crosscorrelation function includes all the crossections of Volterra kernels of the nonlinear system. The problem is how to separate these crossections from each other. This paper proposes two methods for separating these crosssections: one is the suitable selection of M-sequence and the other is amplitude variation method. This identification method is applied to nonlinear model predictive control.

Słowa kluczowe

EN

identification; nonlinear systems; Volterra kernels; model predictive control; M-sequence

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2005
• Tom: Vol. 15, no. 2
• Strony: 137--158
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Kashiwagi H.

• Kumamoto Study Center of The University of The Air, c/o Sojo University, Kumamoto, Japan

autor

Harada H.

• Faculty of Engineering, Kumamoto University, Kurokami 2-39-1, Kumamoto, Japan

Bibliografia

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