An attitude control of a helicopter by adaptive PID controller

Author

Ohnishi, Yoshihiro ; Mori, Shinsuke

Author_Institution

Fac. of Educ., Ehime Univ., Matsuyama, Japan

fYear

2013

fDate

25-27 Sept. 2013

Firstpage

565

Lastpage

570

Abstract

The effectiveness of neural networks is discussed for nonlinear systems. The radial basis function network (RBFN) is proposed as one of the neural networks. This network has the bases functions, therefore, the large value can be obtained in the neighborhood of the training data. In this paper, the RBFN is utilized for the purpose of nonlinear compensation. That is, the control parameters are tuned by RBFN. First, the suitable values are learned for RBFN by using the input and output data. Finally, the practically and utility of proposed method is discussed through the experimental evaluation of the infrared-controlled model helicopter.

Keywords

adaptive control; attitude control; helicopters; neurocontrollers; three-term control; RBFN; adaptive PID controller; attitude control; infrared controlled model helicopter; neural networks; nonlinear compensation; nonlinear systems; radial basis function network; Helicopters; Neural networks; Nose; Simulation; Training; Training data; Uncertainty; PID control; RBFN; nonlinear systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Mechatronic Systems (ICAMechS), 2013 International Conference on

Conference_Location

Luoyang

Print_ISBN

978-1-4799-2518-6

Type

conf

DOI

10.1109/ICAMechS.2013.6681707

Filename

6681707