Identification of composite local linear state-space models using a projected gradient search
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Publication:4800343
DOI10.1080/0020717021000023807zbMath1022.93012OpenAlexW2027213377WikidataQ59591954 ScholiaQ59591954MaRDI QIDQ4800343
Vincent Verdult, Verhaegen, Michel, Lennart Ljung
Publication date: 20 October 2003
Published in: International Journal of Control (Search for Journal in Brave)
Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-55806
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Cites Work
- An iterative learning control theory for a class of nonlinear dynamic systems
- A local model networks based multivariable long-range predictive control strategy for thermal power plants
- Iterative learning control for a class of nonlinear systems
- Iterative learning control in feedback systems
- Adaptive robust iterative learning control with dead zone scheme
- Constructing NARMAX models using ARMAX models
- Design and analysis of gain-scheduled control using local controller networks
- Nonlinear modelling and control of electrically stimulated muscle: A local model network approach
- Constrained quadratic stabilization of discrete-time uncertain non-linear multi-model systems using piecewise affine state-feedback
- Non-linear control system design via fuzzy modelling and LMIs
- On the interpretation of local models in blended multiple model structures
- Survey of gain-scheduling analysis and design
- Analysis of gain scheduled control for nonlinear plants
- Iterative learning control synthesis based on 2-D system theory
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