Adaptive neural controller based on convex parametrization

• Main Author: Patkar, Abhishek.
• Other Authors: A. M.Annaswamy.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2021
• Subjects: Mechanical Engineering.
• Online Access: https://hdl.handle.net/1721.1/128972

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