Decentralized neuro-fuzzy control of a class of nonlinear systems
Por:
Hernández M.A., Tang Y.
Publicada:
1 ene 2008
Categoría:
Industrial and Manufacturing Engineering
Resumen:
A decentralized control based on recurrent neuro-fuzzy networks is proposed for a class of nonlinear systems. It consists of an adaptive component and a uncertainty compensation component. First the control law is designed using the state feedback, and the semiglobal stability is established. Then, by means of a highgain observer, this control law uses only the output feedback. The main features of the proposed scheme are its robustness against uncertainties and its simplicity of implementation. To illustrate the proposed scheme, experiments on a 2-degree-of-freedom robot are included. © 2008 Springer Science+Business Media, LLC.
Filiaciones:
Hernández M.A.:
Faculty of Engineering, National University of Mexico, FI-UNAM, P.O. Box 70-273, 04510 Mexico DF, Mexico
Tang Y.:
Faculty of Engineering, National University of Mexico, FI-UNAM, P.O. Box 70-273, 04510 Mexico DF, Mexico
|