Nonlinear Discrete-Time Adaptive Controller Based on Fuzzy Rules Emulated Network and Its Estimated Gradient

Author

Treesatayapun, Chidentree ; Vega, Vicente Parra ; Sanchez, F.J.R.

Author_Institution

Dept. of Robotic & Manuf., CINVESTAV, Saltillo, Mexico

fYear

2008

fDate

11-13 Dec. 2008

Firstpage

892

Lastpage

895

Abstract

The adaptive controller for a class of nonlinear discrete-time systems based on multi-input fuzzy rules emulated network (MIFREN) is introduced in this article. MIFREN is assigned to identify the unknown plant under control, then a novel control law is introduced based the previously identified plant with another MIFREN. All control parameters, including the learning rates are selected to guarantee bounded close-loop signals, via Lyapunov stability criteria. The performance of the proposed control algorithm is demonstrated by computer simulation results.

Keywords

Lyapunov methods; adaptive control; closed loop systems; discrete time systems; fuzzy control; gradient methods; learning (artificial intelligence); nonlinear control systems; stability criteria; Lyapunov stability criteria; MIFREN; bounded close-loop signal; gradient method; learning rate; multiinput Fuzzy rules emulated network; nonlinear discrete-time adaptive controller; Adaptive control; Automatic control; Computer simulation; Control systems; Fuzzy control; Fuzzy systems; Nonlinear control systems; Programmable control; Sliding mode control; System identification; Fuzzy logic; Neural networks; Nonlinear discrete-time;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Conference_Location

San Diego, CA

Print_ISBN

978-0-7695-3495-4

Type

conf

DOI

10.1109/ICMLA.2008.108

Filename

4725088