Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer (Π-MPC)
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and compensates for periodic disturbances. We present the design of the observer and the accompanying tracking MPC scheme, proving that their combination achieves zero tracking error asymptotically, regardless of the complexity of the unmodelled dynamics. We validate the effectiveness of our method, demonstrating asymptotically perfect tracking on a high-dimensional soft robot with nearly 10,000 states and a fivefold reduction in tracking errors compared to a baseline MPC on small-scale autonomous race car experiments.
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Publication status
published
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Book title
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
12840 - 12847
Publisher
IEEE
Event
37th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
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Methods
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Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)
Related publications and datasets
Is supplemented by: https://github.com/StanfordASL/Pi-MPCIs supplemented by: https://youtu.be/vBgiodXCQVQ