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Identification schemes for unmanned excavator arm parameters

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Abstract

Parameter identification is a key requirement in the field of automated control of unmanned excavators (UEs). Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters (mass, inertia, and length) and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Mu’tah University, Karak, Jordan

    Yahya H. Zweiri

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  1. Yahya H. Zweiri
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Correspondence to Yahya H. Zweiri.

Additional information

This work was supported by the EPSRC (No. GR/R50738/01).

Yahya H. Zweiri is an assistant professor in the Department of Mechanical Engineering, Mu’tah University, Jordan. He received the Ph.D. degree from the Department of Mechanical Engineering, King’s College London, UK, in 2003. He has published over 18 refereed research papers in international journals.

He has over 9 years research experience in the machine intelligence, modelling, and estimation of mechanical systems.

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Zweiri, Y.H. Identification schemes for unmanned excavator arm parameters. Int. J. Autom. Comput. 5, 185–192 (2008). https://doi.org/10.1007/s11633-008-0185-x

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  • DOI: https://doi.org/10.1007/s11633-008-0185-x

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