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Precision Control and Simulation Verification of Hydraulic Manipulator under Unknown Load

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Intelligent Robotics and Applications (ICIRA 2023)

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Abstract

Hydraulic manipulators with multiple degrees of freedom have an increasing range of applications in industrial production, and have the potential to complete various complex tasks. Nowadays, the complex and precision-demanding working environment often requires the hydraulic manipulators to have high control accuracy, which limits their usage. The inherent high-order nonlinearity and uncertainty of hydraulic systems greatly affect the precise control of hydraulic manipulators. High order dynamics such as friction and vibration also have a significant impact on their controlling. Additionally, manipulator typically operates in complex environments with large loads, the end load has always been an important external factor affecting their control accuracy. In this paper, a direct/indirect adaptive robust controller (DIARC) is developed. By incorporating nonlinear and uncertain terms into the dynamic modeling, the control accuracy can be improved. In addition, the end load of the manipulator is incorporated into the parameter space, which makes end load estimation possible. Simulation is constructed using MATLAB Simscape and the performance of the controller is attested. Results shows that the controller can not only improve the tracking accuracy when the end load is heavy, but precisely identify the mass of end load precisely.

Q. Wang and M. Qi—These authors contributed equally to this work.

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Acknowledgement

This work is supported by National Natural Science Foundation of China (Nos. 52075476), Zhejiang Provincial Natural Science Foundation of China (No. LR23E050001), and Fundamental Research Funds for the Central Universities (226–2023-00029).

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Correspondence to Zheng Chen .

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Wang, Q., Qi, M., Xia, Y., Chen, Z. (2023). Precision Control and Simulation Verification of Hydraulic Manipulator under Unknown Load. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14271. Springer, Singapore. https://doi.org/10.1007/978-981-99-6495-6_32

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  • DOI: https://doi.org/10.1007/978-981-99-6495-6_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6494-9

  • Online ISBN: 978-981-99-6495-6

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