Stable predictive control based on recurrent fuzzy neural networks

Author

Lu, Chi-Huang ; Liu, Chi-Ming ; Charng, Yuan-Hai

Author_Institution

Dept. of Electr. Eng., Hsiuping Inst. of Technol., Taichung, Taiwan

fYear

2011

fDate

15-18 May 2011

Firstpage

897

Lastpage

901

Abstract

This paper presents a design method for stable predictive control (SPC) of nonlinear discrete-time system via recurrent fuzzy neural networks (RFNNs). An RFNN-based predictive control law is derived based on the minimization of a modified predictive performance criterion. Two theorems are presented for the stability and steady-state performance of the closed-loop systems. The results from numerical simulations show that the proposed SPC method is capable of controlling nonlinear systems with satisfactory performance under setpoint and disturbance changes.

Keywords

closed loop systems; control system synthesis; discrete time systems; fuzzy neural nets; minimisation; nonlinear control systems; numerical analysis; predictive control; recurrent neural nets; stability; closed loop systems; minimization; modified predictive performance criterion; nonlinear discrete time system; numerical simulations; recurrent fuzzy neural networks; stability; stable predictive control design method; Artificial neural networks; Control systems; Fuzzy control; Fuzzy neural networks; Nonlinear systems; Predictive control; Predictive models; Generalized predictive control; nonlinear discrete-time system; recurrent fuzzy neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ASCC), 2011 8th Asian

Conference_Location

Kaohsiung

Print_ISBN

978-1-61284-487-9

Electronic_ISBN

978-89-956056-4-6

Type

conf

Filename

5899191