Adaptive output-feedback decentralized control of a class of second order nonlinear systems using recurrent fuzzy neural networks

Authors:
Miguel Hernandez;Yu Tang
Affiliations:
Faculty of Engineering, National University of Mexico, Mexico City, Mexico;Faculty of Engineering, National University of Mexico, Mexico City, Mexico
Venue:
Neurocomputing
Year:
2009

Citing 7
Cited 7

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Abstract

In this paper the design of an adaptive output-feedback decentralized control for the class of second order nonlinear affine interconnected systems based on recurrent fuzzy neural networks (RFNN) is addressed. First, a centralized control that needs the state measurements of all subsystems is designed. Then a decentralized control using the local state measurements is obtained by adding a control component aimed at compensating for the interconnections. Finally, an adaptive output-feedback decentralized control based on an RFNN is designed. In design of such controller, no separated state estimator is needed, since the controller dynamics is embedded in the recurrent network. Practical tracking is established by invoking Lyapunov stability analysis. Simulation and experimental results are presented to evaluate the performance of the proposed control law.