An Application of Non-Iterative Identification Method to Structural Parameter Estimation

Engineering structures usually exhibit time-varying behavior when subjected to strong excitation or due to material deterioration. This behavior is one of the key properties affecting the structural performance. Hence, reasonable description and timely tracking of time-varying characteristics of engineering structures are necessary for their safety assessment and life-cycle management. Due to its powerful ability of approximating functions in the time–frequency domain, wavelet multi-resolution approximation has been widely applied in the field of parameter estimation. Considering that the damage levels of beams and columns are usually different, identification of time-varying structural parameters of frame structure under seismic excitation using wavelet multi-resolution approximation is studied in this article. A time-varying dynamical model including both the translational and rotational degrees of freedom is established so as to estimate the stiffness coefficients of beams and columns separately. By decomposing each time-varying structural parameter using one wavelet multi-resolution approximation, the time-varying parametric identification problem is transformed into a time-invariant non-parametric one. In solving the high number of regressors in the non-parametric regression program, the modified orthogonal forward regression algorithm is proposed for significant term selection and parameter estimation. This work is demonstrated through numerical examples which consider both gradual variation and abrupt changes in the structural parameters.

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Experimental Robot Identification: Advantages of Combining Internal and External Measurements and of Using Periodic Excitation

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.1409936 ◽

2001 ◽

Vol 123 (4) ◽

pp. 630-636 ◽

Cited By ~ 16

Author(s):

Walter Verdonck ◽

Jan Swevers ◽

Jean-Claude Samin

Keyword(s):

Signal Processing ◽

Parameter Estimation ◽

Effective Means ◽

Base Plate ◽

Experimental Results ◽

Parameter Estimates ◽

Periodic Excitation ◽

Identification Method ◽

Reaction Forces ◽

Improved Model

This paper discusses the advantages of using periodic excitation and of combining internal and external measurements in experimental robot identification. This discussion is based on the robot identification method developed by Swevers et al., a method that is recognized by industry as an effective means of robot identification that is frequently used, Hirzinger, G., Fischer, M., Brunner, B., Koeppe, R., Otter, M., Grebenstein, M., and Schafer, I, 1999, “Advances is Robotics: The DLR Experiment,” The International Journal of Robotics Research, Vol. 18, No. 11, pp. 1064–1087 [3]. Experimental results on a KUKA IR 361 show that the periodicity of the robot excitation is a key element of this method. Nonperiodic robot excitation complicates the signal processing preceding the parameter estimation, often yielding less accurate parameter estimates. An extension of this identification method combines internal and external measurements, Chenut, X., Samin, J. C., Swevers, J., and Ganseman, C., 2000, “Combining Internal and External robot Models for improved Model Parameter Estimation,” Mechanical Systems and Signal Processing. Vol. 14, No. 5, pp. 691–704 [4], yielding robot models that allow to accurately predict the actuator torques and the reaction forces/torques of the robot on its base plate, which are both important for the path planning. This paper presents and critically discusses the first experimental results obtained with this method.

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