The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Canonical variate analysis in identification, filtering, and adaptive control
Abstract
The canonical variate analysis (CVA) approach for system identification, filtering, and adaptive control is developed. The past/future Markov property provides a starting point for defining a reduced-order prediction problem. The solution is a canonical variate analysis that is characterized by a generalized singular value decomposition. State-space model estimation requires only simple regression, and state order selection involves the optimal Akaike information criterion procedure. The CVA method extends to time-varying and abruptly changing systems. A reduce-rank stochastic model predictive control problem is shown to be equivalent to the CVA problem. Also discussed are computational aspects, applications, and an example illustrating the method. New extensions to the identification of general nonlinear systems are briefly discussed. The CVA method provides an approach giving reliable, automatic implementation of identification, filtering, and control for online adaptive control.>
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Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
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Detection and Differentiation of Replay Attack and Equipment Faults in SCADA Systems
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Metadata
- Authors
- Larimore, W. E.
- Venue
- 29th IEEE Conference on Decision and Control
- Publisher
- IEEE
- Published
- 1990
- Pages
- 596-604 vol.2
- ISSN
- 0191-2216 (ISSN-L)
- OpenAlex
- W2109869845
- Cited by
- 573
- References
- 16
- FWCI
- 9.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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