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Abstract
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- The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the design of low complexity controllers from a statistical perspective. It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy. This does not necessarily mean that a full-order model always is necessary as well designed experiments allow for restricted complexity models to be near-optimal. Experiment design can therefore be seen as the key to successful applications. For this reason, particular attention is given to the interaction between experimental constraints and performance specifications.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- identification for control
- statistical analysis
- experiment design
- model validation
- adaptive robust-control
- asymptotic variance expressions
- h-infinity identification
- error parameter-estimation
- system-identification
- nonlinear-systems
- iterative identification
- model validation
- recursive algorithms
- orthonormal bases
- SRA - ICT
- SRA - Informations- och kommunikationsteknik
Publication and Content Type
- ref (subject category)
- art (subject category)
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