Bridging the Model-Reality Gap With Lipschitz Network Adaptation

As robots venture into the real world, they are subject to unmodeled dynamics and disturbances. Traditional model-based control approaches have been proven successful in relatively static and known operating environments. However, when an accurate model of the robot is not available, model-based des...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 7; H. 1; S. 642 - 649
Hauptverfasser:	Zhou, Siqi, Pereida, Karime, Zhao, Wenda, Schoellig, Angela P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation Adaptation models Aerodynamics Computational modeling Control systems design deep learning methods Dynamic stability Dynamical systems Hovering Lipschitz condition Machine learning for robot control Model reference adaptive control Neural networks Nonlinear control Nonlinear dynamical systems Nonlinear dynamics Nonlinear systems Pendulums Predictive models Robots robust/adaptive control Uncertainty
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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