Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 5; H. 2; S. 3049 - 3056
Hauptverfasser:	Nubert, Julian, Kohler, Johannes, Berenz, Vincent, Allgower, Frank, Trimpe, Sebastian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Constraints Deep learning in robotics and automation Dynamic stability Electron tubes Feedback control Manipulators motion control Network control Neural networks optimization and optimal control Predictive control redundant robots Robot arms Robotics Robust control robust/adaptive control of robotic systems Robustness Run time (computers) Safety Statistical methods Task analysis Task complexity Tracking Uncertainty
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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