Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space
Accurate identification of minimum set of dynamics parameters is required for high-precision and high-speed motion control. The identification uses the dynamical model and its motion data. This motion data does not always satisfy the equations in the dynamical model because of unmodeled dynamics and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Japanese |
| Published: |
The Japan Society of Mechanical Engineers
2022-10-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/88/914/88_22-00100/_pdf/-char/en |
| _version_ | 1811225141929050112 |
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| author | Masafumi OKADA Kazuki WATANABE Ken MASUYA |
| author_facet | Masafumi OKADA Kazuki WATANABE Ken MASUYA |
| author_sort | Masafumi OKADA |
| collection | DOAJ |
| description | Accurate identification of minimum set of dynamics parameters is required for high-precision and high-speed motion control. The identification uses the dynamical model and its motion data. This motion data does not always satisfy the equations in the dynamical model because of unmodeled dynamics and unexpected noise. The least squares method is generally used for approximated model. It may well satisfy the equations in the dynamical model, however, the optimality as a model for control system design has to be discussed. In this paper, we propose a stochastic identification method of minimum set of dynamic parameters. In conventional least squares method, the error of dynamic equation is assumed to be white gaussian and its square mean is minimized while in the proposed method, the error is assumed to be due to parameter fluctuation, and its covariance is optimized so that the sensitivity of velocity field in the state space with respect to dynamic parameter is small, which means advantageous parameter for controlled system. The simulation and experimental results show the effectiveness of the proposed method. |
| first_indexed | 2024-04-12T09:01:55Z |
| format | Article |
| id | doaj.art-bc09827959f04ac899e1f9857f7dff58 |
| institution | Directory Open Access Journal |
| issn | 2187-9761 |
| language | Japanese |
| last_indexed | 2024-04-12T09:01:55Z |
| publishDate | 2022-10-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Nihon Kikai Gakkai ronbunshu |
| spelling | doaj.art-bc09827959f04ac899e1f9857f7dff582022-12-22T03:39:13ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612022-10-018891422-0010022-0010010.1299/transjsme.22-00100transjsmeStochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-spaceMasafumi OKADA0Kazuki WATANABE1Ken MASUYA2Department of Mechanical Engineering, Tokyo Institute of TechnologyDepartment of Mechanical Engineering, Tokyo Institute of TechnologyDepartment of Mechanical Engineering, Tokyo Institute of TechnologyAccurate identification of minimum set of dynamics parameters is required for high-precision and high-speed motion control. The identification uses the dynamical model and its motion data. This motion data does not always satisfy the equations in the dynamical model because of unmodeled dynamics and unexpected noise. The least squares method is generally used for approximated model. It may well satisfy the equations in the dynamical model, however, the optimality as a model for control system design has to be discussed. In this paper, we propose a stochastic identification method of minimum set of dynamic parameters. In conventional least squares method, the error of dynamic equation is assumed to be white gaussian and its square mean is minimized while in the proposed method, the error is assumed to be due to parameter fluctuation, and its covariance is optimized so that the sensitivity of velocity field in the state space with respect to dynamic parameter is small, which means advantageous parameter for controlled system. The simulation and experimental results show the effectiveness of the proposed method.https://www.jstage.jst.go.jp/article/transjsme/88/914/88_22-00100/_pdf/-char/enparameter identificationsensitivity analysisinverted pendulumvector fielddynamical system |
| spellingShingle | Masafumi OKADA Kazuki WATANABE Ken MASUYA Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space Nihon Kikai Gakkai ronbunshu parameter identification sensitivity analysis inverted pendulum vector field dynamical system |
| title | Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space |
| title_full | Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space |
| title_fullStr | Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space |
| title_full_unstemmed | Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space |
| title_short | Stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state-space |
| title_sort | stochastic identification of minimum set of dynamics parameters that has small sensitivity on controlled velocity field in state space |
| topic | parameter identification sensitivity analysis inverted pendulum vector field dynamical system |
| url | https://www.jstage.jst.go.jp/article/transjsme/88/914/88_22-00100/_pdf/-char/en |
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