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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| Vydané v: | IEEE robotics and automation letters Ročník 5; číslo 2; s. 3049 - 3056 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | 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, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems. |
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| AbstractList | 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, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems. |
| Author | Nubert, Julian Berenz, Vincent Allgower, Frank Kohler, Johannes Trimpe, Sebastian |
| Author_xml | – sequence: 1 givenname: Julian orcidid: 0000-0001-8949-6134 surname: Nubert fullname: Nubert, Julian email: nubertj@ethz.ch organization: Intelligent Control Systems Group, Max Planck Institute for Intelligent Systems, Stuttgart, Germany – sequence: 2 givenname: Johannes orcidid: 0000-0002-5556-604X surname: Kohler fullname: Kohler, Johannes email: johannes.koehler@ist.uni-stuttgart.de organization: Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany – sequence: 3 givenname: Vincent orcidid: 0000-0003-2226-9660 surname: Berenz fullname: Berenz, Vincent email: vberenz@tuebingen.mpg.de organization: Autonomous Motion Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany – sequence: 4 givenname: Frank orcidid: 0000-0002-3702-3658 surname: Allgower fullname: Allgower, Frank email: frank.allgower@ist.uni-stuttgart.de organization: Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany – sequence: 5 givenname: Sebastian orcidid: 0000-0002-2785-2487 surname: Trimpe fullname: Trimpe, Sebastian email: strimpe@tuebingen.mpg.de organization: Intelligent Control Systems Group, Max Planck Institute for Intelligent Systems, Stuttgart, Germany |
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| References | ref13 ref15 ref14 ref2 ref17 nubert (ref24) 2019 ref16 köhler (ref8) 2019 ref19 ref18 karg (ref11) 2018 köhler (ref5) 2019 siciliano (ref1) 2008 ref23 rawlings (ref3) 2009 ref26 zhang (ref25) 2017 ref20 zhang (ref10) 2019 ref22 ref21 ref27 chen (ref12) 2019 ref7 ref9 ref4 ref6 |
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| SubjectTerms | 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 |
| Title | Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control |
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