NDLI: Neural feedback linearization and its application in radar servo systems

Content Provider	IEEE Xplore Digital Library
Author	Lou Shuntian Chen Xinbai Zhang Xianda
Copyright Year	2000
Description	Author affiliation: Key Lab. for Radar Signal Process., Xidian Univ., Xi'an, China (Lou Shuntian)
Abstract	This paper proposes a feedback linearized design method for a kind of affine nonlinear system with unknown nonlinearity and/or uncertainty. A neural network is used to approximate the unknown function and/or uncertainty in the system. Then the neural feedback linearized controller without off-line training is obtained using conventional feedback linearization. According to the Lyapunov stability theory, the weight update law of neural network, the adaptive law of error bound and the feedback control are obtained, thus the stability of closed-loop control system is ensured. This design method can be applied to radar servo system. The novel radar servo system using neural feedback linearization is constructed and its effectiveness is verified through simulations.
Starting Page	1022
Ending Page	1025
File Size	255661
Page Count	4
File Format	PDF
ISBN	078035995X
DOI	10.1109/WCICA.2000.863390
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-06-26
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurofeedback Radar applications Servomechanisms Design methodology Uncertainty Neural networks Nonlinear systems Linear feedback control systems Lyapunov method Adaptive systems
Content Type	Text
Resource Type	Article

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