Research on Multi-parameter identification of underwater robot joint motor

SHI Lin; HU Qiao; SHI Xindong; SUN Liangjie; ZHANG jian; Liu Haiyang

doi:10.11993/j.issn.2096-3920.2024-0062

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Article Navigation > Journal of Unmanned Undersea Systems > 2024 > Accepted Manuscript

SHI Lin, HU Qiao, SHI Xindong, SUN Liangjie, ZHANG jian, Liu Haiyang. Research on Multi-parameter identification of underwater robot joint motor[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0062

Citation:

SHI Lin, HU Qiao, SHI Xindong, SUN Liangjie, ZHANG jian, Liu Haiyang. Research on Multi-parameter identification of underwater robot joint motor[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0062

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Research on Multi-parameter identification of underwater robot joint motor

doi: 10.11993/j.issn.2096-3920.2024-0062

1.
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2.
Shanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an 710049, China

Received Date: 2024-04-03
Accepted Date: 2024-05-11
Rev Recd Date: 2024-04-29

Available Online: 2024-10-28

Abstract

Abstract

With the rapid development of underwater unmanned systems, underwater joint motor plays an important role as the core driving device of underwater robot, underwater robot arm and other underwater equipment. In this paper, the multi-parameter on-line identification of underwater articulated motor is studied to solve the problem that the precision and stability of motor control are deteriorated due to the change of motor parameters under the influence of different working environments. Specifically, the method of increasing steady-state operating points is used to realize multi-parameter full rank identification. At the same time, in order to improve the accuracy and robustness of the identification method, the feasibility of Extended Kalman filter(EKF) and H-infinity filter (HIF) in the identification of motor parameters was studied. Then a new method based on EKF and H infinite joint estimation is proposed. Through simulation comparison, it is found that in parameter identification, the steady-state standard deviation of the proposed joint estimation method is reduced by 84.7% compared with the adaptive EKF method, and the accuracy is increased by 91.7% compared with the adaptive HIF method. The robustness and accuracy of the proposed method based on EKF and H infinite joint estimation are verified, which provides theoretical and technical support for the stable and efficient operation of the underwater joint motor.
- permanent magnet synchronous motor,
- parameter identification,
- Kalman filtering,
- H infinite filtering

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References(23)

References

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