Differential Recurrent Neural Network Based Model Predictive Control for the Control of MAV Attitude

Author

Chen, Xiangjian ; Xu, Zhijun ; Li, Di ; Long, Kehui

Author_Institution

Chang Chun Inst. of Opt. Fine Mech. & Phys., Chinese Acad. of Sci., Changchun, China

fYear

2010

fDate

22-23 May 2010

Firstpage

1

Lastpage

3

Abstract

An efficient differential recurrent neural network is developed in this paper, and the trained network can be used in the nonlinear model predictive control, and also predict the future dynamic behavior of the nonlinear process in real time. In the new training network, use Taylor series expansion and automatic differentiation techniques. The effectiveness of the differential recurrent neural network predictive model training and predictive controller demonstrated through the MAV attitude control. The differential recurrent neural network-based NMPC approach results in good control performance.

Keywords

aerospace control; attitude control; neurocontrollers; nonlinear control systems; predictive control; recurrent neural nets; remotely operated vehicles; MAV attitude control; NMPC approach; Taylor series expansion; automatic differentiation techniques; differential recurrent neural network; micro air vehicle; nonlinear model predictive control; Attitude control; Automatic control; Neural networks; Nonlinear optics; Optical computing; Optical fiber networks; Physics; Predictive control; Predictive models; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5872-1

Electronic_ISBN

978-1-4244-5874-5

Type

conf

DOI

10.1109/IWISA.2010.5473505

Filename

5473505