Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4636339 | Applied Mathematics and Computation | 2007 | 11 Pages |
Abstract
For multi-input, single-output output-error systems, a difficulty in identification is that the information vector in the identification model obtained contains unknown inner/intermediate variables; thus the standard least squares methods cannot be applied directly. In this paper, we present a multi-innovation least squares identification algorithm based on the auxiliary model; its basic idea is to replace the unknown inner variables with their estimates computed by an auxiliary model. Convergence analysis indicates that the parameter estimation error converges to zero under persistent excitation. The algorithm proposed has significant computational advantage over existing identification algorithms. A simulation example is included.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Feng Ding, Huibo Chen, Ming Li,