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Adaptive neural controller based on convex parametrization

Author(s)
Patkar, Abhishek.
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Download1227044482-MIT.pdf (1.662Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Advisor
A. M.Annaswamy.
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. http://dspace.mit.edu/handle/1721.1/7582
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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).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/128972
Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Publisher
Massachusetts Institute of Technology
Keywords
Mechanical Engineering.

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  • Mechanical Engineering - Master's degree
  • Mechanical Engineering - Master's degree

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