Several recursive techniques for observer/Kalman filter system identification from dataThis paper derives algorithms for identifying autoregressive models, with external input, of multi-input multi-output systems from data using a fast transversal filter or a least-squares lattice filter. The autoregressive models including external inputs are used to identify state-space models and the corresponding observer/Kalman filter gains of the system. The derivation is an extension of scalar autoregressive model approaches, modified to cope with multivariables, external inputs and an extra direct-influence term. Comparisons between the fast transversal filter, the least-squares lattice filter and the classical least-squares method are made in terms of complexity, computational cost and practical applications issues. A numerical example is included to illustrate the approach.
Document ID
19920072564
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Chen, Chung-Wen (NASA Langley Research Center Hampton, VA, United States)
Lee, Gordon (North Carolina State University Raleigh, United States)
Juang, Jer-Nan (NASA Langley Research Center Hampton, VA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1992
Subject Category
Cybernetics
Report/Patent Number
AIAA PAPER 92-4386
Meeting Information
Meeting: AIAA Guidance, Navigation and Control Conference