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


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Adaptive neural controller based on convex parametrization



    Erscheinungsdatum :

    2020-01-01


    Anmerkungen:

    1227044482


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Schlagwörter :

    Klassifikation :

    DDC:    629





    Parametrization of the regular equivalences of the canonical controller

    Julius, A. Agung / Polderman, Jan Willem / Schaft, Arjan van der | BASE | 2008

    Freier Zugriff

    Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control

    Mavrinac, Marko / Lorencin, Ivan / Car, Zlatan et al. | BASE | 2022

    Freier Zugriff

    Robust Controller Design Using Parametrization of Stabilizing State Feedback Gains

    Ohara, A. / Masubuchi, I. / Suda, N. | British Library Online Contents | 1994


    ReACT: Reinforcement Learning for Controller Parametrization Using B-Spline Geometries

    Rudolf, Thomas / Flögel, Daniel / Schürmann, Tobias et al. | BASE | 2024

    Freier Zugriff