Adaptive neural controller based on convex parametrization
Massachusetts Institute of Technology. Department of Mechanical Engineering.
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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.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 65-67).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology