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Proceedings Paper

System Identification And Control Using SVD's On Systolic Arrays
Author(s): Wallace E Larimore; Franklin T Luk
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Paper Abstract

A new class of algorithms based upon a generalized singular value decomposition (SVD) is considered for system identification, statistical model order determination, model order reduction, and predictive control. Currently available algorithms for system identification and control are not completely reliable for automatic implementation on microprocessors in real time. In the generalized SVD approach, the algorithms are computationally stable and numerically accurate and can be implemented on systolic array processors using recently developed algorithms resulting in a considerable speedup. The method is based upon a recent generalized canonical variate analysis (CVA) method for determining the optimal state of a restricted order in system identification, reduced order stochastic filtering, and model predictive control. This permits a unified approach to the solution of these problems from the viewpoints of a prediction problem as well as an approximation problem. Algorithms for online computation in identification, filtering, and control of high order linear multivariable systems are developed. Implementing these algorithms on systolic array processors are discussed.

Paper Details

Date Published: 20 April 1988
PDF: 12 pages
Proc. SPIE 0880, High Speed Computing, (20 April 1988); doi: 10.1117/12.944033
Show Author Affiliations
Wallace E Larimore, Business and Technological Systems (United States)
Franklin T Luk, Cornell University (United States)

Published in SPIE Proceedings Vol. 0880:
High Speed Computing
David P. Casasent, Editor(s)

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