TY - GEN
T1 - High-Frequency Nonlinear Model Predictive Control of a Manipulator
AU - Kleff, Sébastien
AU - Meduri, Avadesh
AU - Budhiraja, Rohan
AU - Mansard, Nicolas
AU - Righetti, Ludovic
N1 - Funding Information:
This work was in part supported by the European Union's Horizon 2020 research and innovation program (grant agreement 780684 and European Research Councils grant 637935) and the National Science Foundation (grants 1825993, 1932187, 1925079 and 2026479).
Funding Information:
1Tandon School of Engineering, New York University, Brooklyn, NY 2LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France 3Max Planck Institute for Intelligent Systems, Tübingen, Germany 4Artificial and Natural Intelligence Toulouse Institute, France This work was in part supported by the European Union’s Horizon 2020 research and innovation program (grant agreement 780684 and European Research Councils grant 637935) and the National Science Foundation (grants 1825993, 1932187, 1925079 and 2026479). We would like to thank Maximilien Naveau, Johannes Pfleging and Julian Viereck for their advice in the implementation and experimental work.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Model Predictive Control (MPC) promises to endow robots with enough reactivity to perform complex tasks in dynamic environments by frequently updating their motion plan based on measurements. Despite its appeal, it has seldom been deployed on real machines because of scaling constraints. This paper presents the first hardware implementation of closed-loop nonlinear MPC on a 7-DoF torque-controlled robot. Our controller leverages a state-of-the art optimal control solver, namely Differential Dynamic Programming (DDP), in order to replan state and control trajectories at real-time rates (1kHz). In addition to this experimental proof of concept, an exhaustive performance analysis shows that our controller outperforms open-loop MPC on a rapid cyclic end-effector task. We also exhibit the importance of a sufficient preview horizon and full robot dynamics through comparisons with inverse dynamics and kinematic optimization.
AB - Model Predictive Control (MPC) promises to endow robots with enough reactivity to perform complex tasks in dynamic environments by frequently updating their motion plan based on measurements. Despite its appeal, it has seldom been deployed on real machines because of scaling constraints. This paper presents the first hardware implementation of closed-loop nonlinear MPC on a 7-DoF torque-controlled robot. Our controller leverages a state-of-the art optimal control solver, namely Differential Dynamic Programming (DDP), in order to replan state and control trajectories at real-time rates (1kHz). In addition to this experimental proof of concept, an exhaustive performance analysis shows that our controller outperforms open-loop MPC on a rapid cyclic end-effector task. We also exhibit the importance of a sufficient preview horizon and full robot dynamics through comparisons with inverse dynamics and kinematic optimization.
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U2 - 10.1109/ICRA48506.2021.9560990
DO - 10.1109/ICRA48506.2021.9560990
M3 - Conference contribution
AN - SCOPUS:85111202086
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 7330
EP - 7336
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -