Adaptive Control of a Class of Nonaffine Systems Using Neural Networks

A neural control synthesis method is considered for a class of nonaffine uncertain single-input-single-output (SISO) systems. The method eliminates a fixed-point assumption and does not assume boundedness on the time derivative of a control effectiveness term. One or the other of these assumptions e...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 18; no. 4; pp. 1149 - 1159
Main Authors Bong-Jun Yang, Calise, A.J.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2007
Subjects
Adaptive control
Algorithms
Computer Simulation
Control design
Control system synthesis
Control systems
Decision Support Techniques
Derivatives
Feedback
Feedback linearization
Mathematical analysis
Models, Theoretical
Network synthesis
Neural networks
Neural Networks (Computer)
Neurocontrollers
nonaffine systems
Nonlinear Dynamics
Nonlinear equations
Open loop systems
Origins
Synthesis
Tracking errors
Uncertainty
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ISSN1045-9227
DOI10.1109/TNN.2007.899253

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Summary:A neural control synthesis method is considered for a class of nonaffine uncertain single-input-single-output (SISO) systems. The method eliminates a fixed-point assumption and does not assume boundedness on the time derivative of a control effectiveness term. One or the other of these assumptions exist in earlier papers on this subject. Using Lyapunov's direct method, it is shown that all the signals of the closed-loop system are uniformly ultimately bounded, and that the tracking error converges to an adjustable neighborhood of the origin. Simulation with a Van Der Pol equation with nonaffine control terms illustrates the approach.
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ISSN:1045-9227
DOI:10.1109/TNN.2007.899253