Bayesian Inference for Path Following Control of Port-Hamiltonian Systems with Training Trajectory Data
Authors
Yuki Okura, Kenji Fujimoto, Ichiro Maruta, Akio Saito, Hidetoshi Ikeda
Abstract
: This paper describes a procedure to design a path following controller of port-Hamiltonian systems based on a training trajectory dataset. The trajectories are generated by human operations, and the training data consist of several trajectories with variations. Hence, we regard the trajectory as a stochastic process model. Then we design a deterministic controller for path following control from the model. In order to obtain reasonable design parameters for a path following controller from the training data, Bayesian inference is adopted in this paper. By using Bayesian inference, we estimate a probability density function of the desired trajectory. Moreover, not only the mean value of the trajectory but also the covariance matrix is acquired. A potential function for path following control is obtained from the probability density function. By incorporating the covariance information into the control system design, it is possible to create a potential function that takes into account uncertainty at each position on the trajectory, and it is expected to construct a control system that generates appropriate assist force for a human operator.
Citation
- Journal: SICE Journal of Control, Measurement, and System Integration
- Year: 2020
- Volume: 13
- Issue: 2
- Pages: 40–46
- Publisher: Informa UK Limited
- DOI: 10.9746/jcmsi.13.40
BibTeX
@article{Okura_2020,
title={{Bayesian Inference for Path Following Control of Port-Hamiltonian Systems with Training Trajectory Data}},
volume={13},
ISSN={1884-9970},
DOI={10.9746/jcmsi.13.40},
number={2},
journal={SICE Journal of Control, Measurement, and System Integration},
publisher={Informa UK Limited},
author={Okura, Yuki and Fujimoto, Kenji and Maruta, Ichiro and Saito, Akio and Ikeda, Hidetoshi},
year={2020},
pages={40--46}
}
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