On Path Following Control of Port-Hamiltonian Systems by Bayesian Inference with Training Trajectory Data

Authors

Yuki Okura, Kenji Fujimoto, Akio Saito, Hidetoshi Ikeda

Abstract

This paper describes a procedure to design a path following controller of port-Hamiltonian systems based on training trajectory data. In order to calculate the reasonable design parameters for path following controller from the training data, Bayesian inference is adopted in this paper. By using Bayesian inference, not only the mean value of the trajectory but also the covariance matrix is acquired. By incorporating the covariance information into the control system design, it is expected to create a potential function that takes into account uncertainty at each position on the trajectory.

Citation

• Journal: 2018 IEEE Conference on Decision and Control (CDC)
• Year: 2018
• Volume:
• Issue:
• Pages: 1956–1960
• Publisher: IEEE
• DOI: 10.1109/cdc.2018.8619261

BibTeX

@inproceedings{Okura_2018,
  title={{On Path Following Control of Port-Hamiltonian Systems by Bayesian Inference with Training Trajectory Data}},
  DOI={10.1109/cdc.2018.8619261},
  booktitle={{2018 IEEE Conference on Decision and Control (CDC)}},
  publisher={IEEE},
  author={Okura, Yuki and Fujimoto, Kenji and Saito, Akio and Ikeda, Hidetoshi},
  year={2018},
  pages={1956--1960}
}

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References

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