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Sensors 2017, 17(2), 311; doi:10.3390/s17020311

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

1
Fraunhofer Research Institute for Mechatronic Systems Design (IEM), Zukunftsmeile 1, 33102 Paderborn, Germany
2
Institute for Robotics and Process Control (IRP), Technische Universität Braunschweig, Mühlenpfordstraße 23, 38106 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Stefan Bosse, Ansgar Trächtler, Klaus-Dieter Thoben, Berend Denkena and Dirk Lehmhus
Received: 23 December 2016 / Revised: 30 January 2017 / Accepted: 1 February 2017 / Published: 8 February 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
View Full-Text   |   Download PDF [6178 KB, uploaded 8 February 2017]   |  

Abstract

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. View Full-Text
Keywords: machine learning; data-driven modeling; mechanical modeling; error models; inverse kinematics; inverse dynamics; learning feed-forward control; soft robot machine learning; data-driven modeling; mechanical modeling; error models; inverse kinematics; inverse dynamics; learning feed-forward control; soft robot
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Reinhart, R.F.; Shareef, Z.; Steil, J.J. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †. Sensors 2017, 17, 311.

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