Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm

High-precision trajectory tracking is fundamental in robotic manipulation. While industrial robots address this through stiffness and high-performance hardware, compliant and cost-effective robots require advanced control to achieve accurate position tracking. In this letter, we present a model-base...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 4; no. 4; pp. 3758 - 3765
Main Authors Carron, Andrea, Arcari, Elena, Wermelinger, Martin, Hewing, Lukas, Hutter, Marco, Zeilinger, Melanie N.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptation models
Algorithms
Control algorithms
Data models
Extended Kalman filter
Feedback linearization
Gaussian process
Gaussian processes
Industrial robots
Inverse dynamics
Learning and adaptive systems
Manipulators
model learning for control
model predictive control
Predictive control
Robot arms
Robot control
Robotics
Service robots
Stiffness
Tracking control
Trajectory control
Uncertainty
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Summary:High-precision trajectory tracking is fundamental in robotic manipulation. While industrial robots address this through stiffness and high-performance hardware, compliant and cost-effective robots require advanced control to achieve accurate position tracking. In this letter, we present a model-based control approach, which makes use of data gathered during operation to improve the model of the robotic arm and thereby the tracking performance. The proposed scheme is based on an inverse dynamics feedback linearization and a data-driven error model, which are integrated into a model predictive control formulation. In particular, we show how offset-free tracking can be achieved by augmenting a nominal model with both a Gaussian process, which makes use of offline data, and an additive disturbance model suitable for efficient online estimation of the residual disturbance via an extended Kalman filter. The performance of the proposed offset-free GPMPC scheme is demonstrated on a compliant 6 degrees of freedom robotic arm, showing significant performance improvements compared to other robot control algorithms.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2929987