NDLI: Adaptive Regulation for a Class of Non-Affine Systems using Neural Network Backstepping with Tuning Functions

Content Provider	IEEE Xplore Digital Library
Author	Bong-Jun Yang Calise, A.J.
Copyright Year	2006
Description	Author affiliation: Sch. of Aerosp. Eng., Georgia Inst. of Technol. Atlanta GA Calise, AJ Sch. of Aerosp. Eng. Georgia Inst. of Technol., GA (Bong-Jun Yang; Calise, A.J.)
Abstract	A backstepping based neural synthesis method is proposed to stabilize a class of non-affine systems, that include non-minimum phase systems as well. The method describes the class of systems in normal form, and uses two neural networks, while previous backstepping methods introduce a neural networks at each backward step. The neural weights are updated using tuning functions, and nonlinear damping terms prevent the functional reconstruction error from propagating to the next backward step. The method does not rely on a fixed-point assumption, nor does it assume that the time derivative of the control effectiveness is bounded (an assumption that is commonly employed when using the mean value theorem). All the signals of the closed-loop system are shown to be uniformly ultimately bounded. Simulation results illustrate the approach
Starting Page	3028
Ending Page	3033
File Size	7392013
Page Count	6
File Format	PDF
ISBN	1424401712
DOI	10.1109/CDC.2006.377166
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Backstepping Uncertainty Control systems Programmable control Adaptive control Damping Error correction Nonlinear dynamical systems USA Councils
Content Type	Text
Resource Type	Article

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