Self-tuning predictive PID controller using wavelet type-2 fuzzy neural networks

Author

Chi-Huang Lu ; Chi-Ming Liu ; Chin-Chi Cheng ; Jheng-Yu Guo

Author_Institution

Dept. of Electr. Eng., Hsiuping Univ. of Sci. & Technol., Taichung, Taiwan

fYear

2012

fDate

16-18 Nov. 2012

Firstpage

181

Lastpage

186

Abstract

This paper presents a predictive proportionalintegral-derivative (PID) controller based on wavelet type-2 fuzzy neural network (WT2FNN) for a class of nonlinear systems. The WT2FNN is employed to estimate the nonlinear function of the controlled system and the predictive PID controller is derived via a predictive performance criterion. The stability analysis of the closed-loop control system is presented by the discrete Lyapunov stability theorem. Numerical simulations that the proposed self-tuning predictive PID control law give satisfactory tracking and disturbance rejection performances.

Keywords

Lyapunov methods; closed loop systems; control system synthesis; fuzzy neural nets; neurocontrollers; nonlinear control systems; numerical analysis; predictive control; stability; three-term control; PID control law; WT2FNN; closed-loop control system; discrete Lyapunov stability theorem; disturbance rejection performance; nonlinear system; numerical simulation; predictive performance criterion; proportional-integral-derivative controller; self-tuning predictive PID controller; stability analysis; tracking performance; wavelet type-2 fuzzy neural network; Control systems; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Neural networks; Nonlinear systems; Predictive control; PID controller; Self-Tuning Control; Type-2 Fuzzy system; Wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Theory and it's Applications (iFUZZY), 2012 International Conference on

Conference_Location

Taichung

Print_ISBN

978-1-4673-2057-3

Type

conf

DOI

10.1109/iFUZZY.2012.6409697

Filename

6409697