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). ; 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. ; by Abhishek Patkar. ; S.M. ; S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
Adaptive neural controller based on convex parametrization
2020-01-01
1227044482
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
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