Automatic Landing Control System Design Using Adaptive Neural Network and Its Hardware Realization

Author

Juang, Jih-Gau ; Chien, Li-Hsiang ; Lin, Felix

Author_Institution

Nat. Taiwan Ocean Univ., Keelung, Taiwan

Volume

5

Issue

2

fYear

2011

fDate

6/1/2011 12:00:00 AM

Firstpage

266

Lastpage

277

Abstract

This paper presents an adaptive neural network, designed to improve the performance of conventional automatic landing systems (ALS). Real-time learning was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. In addition, we applied a DSP controller using the VisSim/TI C2000 Rapid Prototyper to develop an embedded control system and establish on-line real-time control. Simulations show that the proposed control scheme has superior performance to conventional ALS under conditions of wind disturbance of up to 75 ft/s.

Keywords

Lyapunov methods; adaptive control; aerospace control; control system synthesis; learning (artificial intelligence); neurocontrollers; real-time systems; DSP controller; Lyapunov stability; VisSim/TI C2000 rapid prototyper; adaptive neural network; automatic landing control system design; hardware realization; real-time learning; Aerospace control; Aircraft; Atmospheric modeling; Control systems; Convergence; Stability criteria; Adaptive neural network; automatic landing system; intelligent control; wind disturbance;

fLanguage

English

Journal_Title

Systems Journal, IEEE

Publisher

ieee

ISSN

1932-8184

Type

jour

DOI

10.1109/JSYST.2011.2134490

Filename

5742715