TR2013-136

Nonlinear Backstepping Learning-based Adaptive Control of Electromagnetic Actuators with Proof of Stability

    •  Benosman, M., Atinc, G.M., "Nonlinear Backstepping Learning-based Adaptive Control of Electromagnetic Actuators with Proof of Stability", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/​CDC.2013.6760058, December 2013, pp. 1277-1282.
      BibTeX TR2013-136 PDF
      • @inproceedings{Benosman2013dec,
      • author = {Benosman, M. and Atinc, G.M.},
      • title = {Nonlinear Backstepping Learning-based Adaptive Control of Electromagnetic Actuators with Proof of Stability},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2013,
      • pages = {1277--1282},
      • month = dec,
      • doi = {10.1109/CDC.2013.6760058},
      • issn = {0743-1546},
      • url = {https://www.merl.com/publications/TR2013-136}
      • }
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  • Research Area:

    Control

Abstract:

In this paper we present a learning-based adaptive method to solve the problem of robust trajectory tracking for electromagnetic actuators. We propose a learning-based adaptive controller; we merge together a nonlinear backstepping controller that ensures bounded input/bounded states stability, with a model-free multiparameter extremum seeking to estimate online the uncertain parameters of the system. We present a proof of stability of this learning-based nonlinear controller. We show the efficiency of this approach on a numerical example.