Adaptive hybrid neural fuzzy controller using augmented error method

Author

Noaman, Noaman M. ; Omar, A.M.

Author_Institution

Dept. of Comput. Eng., Al-Nahrain Univ., Baghdad, Iraq

fYear

2006

fDate

20-22 March 2006

Firstpage

1

Lastpage

5

Abstract

The design of fuzzy controller can be supported by comparing the signal with the neural network controller. Such approaches are usually called hybrid neural - fuzzy controller or multi controller. The hybrid model is able to compare the signal from the fuzzy controller and neural network learning with back propagation method. In this paper the plant is a DC motor base assembly with Pittman gear head servomotor using in robot and another applications, in order to evaluate the system performance when the motor load is changing. Due to this change the speed of the motor will be decreasing and the plant parameter is changed. Therefore, adaptive hybrid neural fuzzy controller is designed to adapt this system change using augmented error method.

Keywords

DC motors; adaptive control; backpropagation; fuzzy control; machine control; neurocontrollers; servomotors; DC motor; Pittman gear head servomotor; adaptive hybrid neural fuzzy controller; augmented error method; back propagation method; motor load; motor speed; multi controller; neural network controller; neural network learning; plant parameter; robot applications; system performance evaluate; Adaptation model; Adaptive systems; Artificial neural networks; Equations; Mathematical model; Signal generators; Transfer functions; Adaptive hybrid neural fuzzy; Augmented error method; Fuzzy controller; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

GCC Conference (GCC), 2006 IEEE

Conference_Location

Manama

Print_ISBN

978-0-7803-9590-9

Electronic_ISBN

978-0-7803-9591-6

Type

conf

DOI

10.1109/IEEEGCC.2006.5686250

Filename

5686250