| Summary: | 碩士 === 國立臺灣大學 === 機械工程學研究所 === 105 === Modal testing is essential for the identification of important dynamical parameters of a mechanical system. However, commercially available modal testing packages are expensive and nonflexible. This thesis aims to employ the popular and powerful package MATLAB as the environment to develop a modal testing program that can be customized to meet the user’s needs. The efficiency of a modal testing program highly depends on the curve fitting algorithm used. Two different curve fitting algorithms, the rational fraction polynomials (RFP) method and the multi-objective genetic algorithms, are adopted. The effectiveness of these two methods on parameter identification is compared.
The RFP method is based on the fact that the frequency response function (FRF) of a linear time-invariant system is a rational function in frequency. The lease squares method is used to determine the coefficients of the numerator and denominator polynomials. The RFP method can be classified into two different types, called the local curve fitting and global curve fitting, according to whether the FRFs are processed sequentially or simultaneously.
The non-dominated sorting genetic algorithm-II (NSGA-II) is used to realize the multi-objective optimization. This algorithm employs the non-dominated sorting and crowding distance to select elite individuals for the next generation. In this case, the genetic diversity is maintained, early convergence to a local extrema is avoided, and high computational efficiency is achieved.
In this thesis, we develop programs based on RFP and NSGAII. Some benchmark tests, for example, modes with nodes, high damping ratios, and double roots, which may present difficulties for parameter identification are used to evaluate these two methods. Possible guidelines to improve these two methods are proposed.
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