Regular Papers

International Journal of Control, Automation and Systems 2009; 7(1): 123-132

Published online March 12, 2009

https://doi.org/10.1007/s12555-009-0115-x

© The International Journal of Control, Automation, and Systems

A New Approach to Fuzzy Modeling and Control for Nonlinear Dynamic Systems: Neuro-Fuzzy Dynamic Characteristic Modeling and Adaptive Control Mechanism

Xiong Luo, Zengqi Sun, and Fuchun Sun

University of Science and Technology Beijing, China

Abstract

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.

Keywords Adaptive control, fuzzy control, neuro-fuzzy dynamic characteristic modeling, nonlinear system.

Article

Regular Papers

International Journal of Control, Automation and Systems 2009; 7(1): 123-132

Published online February 1, 2009 https://doi.org/10.1007/s12555-009-0115-x

Copyright © The International Journal of Control, Automation, and Systems.

A New Approach to Fuzzy Modeling and Control for Nonlinear Dynamic Systems: Neuro-Fuzzy Dynamic Characteristic Modeling and Adaptive Control Mechanism

Xiong Luo, Zengqi Sun, and Fuchun Sun

University of Science and Technology Beijing, China

Abstract

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.

Keywords: Adaptive control, fuzzy control, neuro-fuzzy dynamic characteristic modeling, nonlinear system.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

Stats or Metrics

Share this article on

  • line

Related articles in IJCAS

IJCAS

eISSN 2005-4092
pISSN 1598-6446