Multivariable frequency domain identification via 2-norm minimizationThe author develops a computational approach to multivariable frequency domain identification, based on 2-norm minimization. In particular, a Gauss-Newton (GN) iteration is developed to minimize the 2-norm of the error between frequency domain data and a matrix fraction transfer function estimate. To improve the global performance of the optimization algorithm, the GN iteration is initialized using the solution to a particular sequentially reweighted least squares problem, denoted as the SK iteration. The least squares problems which arise from both the SK and GN iterations are shown to involve sparse matrices with identical block structure. A sparse matrix QR factorization method is developed to exploit the special block structure, and to efficiently compute the least squares solution. A numerical example involving the identification of a multiple-input multiple-output (MIMO) plant having 286 unknown parameters is given to illustrate the effectiveness of the algorithm.
Document ID
19930038866
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Bayard, David S. (JPL Pasadena, CA, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: 1992 American Control Conference, 11th, Chicago, IL, June 24-26, 1992, Proceedings. Vol. 2 (A93-22776 07-63)
Publisher: Institute of Electrical and Electronics Engineers