Adaptive neural controller based on convex parametrization
Name
1227044482-MIT.pdf
Size
1.66 MB
Format
Adobe PDF
Checksum (MD5)
6dbbec2be0e21a74e385281d71cba78b
Author(s)
Patkar, Abhishek.
Advisor(s)
A. M.Annaswamy.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control to lead to global boundedness. We revisit this problem in this thesis and suggest a specific class of neural networks that employ convex activation functions. By using the algorithms that have been proposed previously for adaptive control in the presence of convex/concave parameterization for adjusting the weights of the neural network, it is shown that global boundedness of all signals can be achieved together with a better tracking error than non-adaptive controllers. It is also shown through simulation studies of an aircraft landing problem that the proposed adaptive controller can lead to better learning of the underlying nonlinearity.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 65-67).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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