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Control-oriented Modelling and Identification: Theory and practice
The Institution of Engineering and Technology, 2015 eISBN: 978-1-84919-615-4 | Cloth: 978-1-84919-614-7 Library of Congress Classification TJ213.C57245 2015 Dewey Decimal Classification 629.89
ABOUT THIS BOOK | TOC
ABOUT THIS BOOK
This comprehensive book covers the state-of-the-art in control-oriented modelling and identification techniques. With contributions from leading researchers in the subject, Control-oriented Modelling and Identification: Theory and practice covers the main methods and tools available to develop advanced mathematical models suitable for control system design, including: object-oriented modelling and simulation; projection-based model reduction techniques; integrated modelling and parameter estimation; identification for robust control of complex systems; subspace-based multi-step predictors for predictive control; closed-loop subspace predictive control; structured nonlinear system identification; and linear fractional LPV model identification from local experiments using an H1-based glocal approach. See other books on: Automatic control | Control theory | Identification | Mathematical models | System design See other titles from The Institution of Engineering and Technology |
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Nearby on shelf for Mechanical engineering and machinery / Control engineering systems. Automatic machinery (General):
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Control-oriented Modelling and Identification: Theory and practice
ABOUT THIS BOOK | TOC
The Institution of Engineering and Technology, 2015
eISBN: 978-1-84919-615-4
Cloth: 978-1-84919-614-7
eISBN: 978-1-84919-615-4
Cloth: 978-1-84919-614-7
This comprehensive book covers the state-of-the-art in control-oriented modelling and identification techniques. With contributions from leading researchers in the subject, Control-oriented Modelling and Identification: Theory and practice covers the main methods and tools available to develop advanced mathematical models suitable for control system design, including: object-oriented modelling and simulation; projection-based model reduction techniques; integrated modelling and parameter estimation; identification for robust control of complex systems; subspace-based multi-step predictors for predictive control; closed-loop subspace predictive control; structured nonlinear system identification; and linear fractional LPV model identification from local experiments using an H1-based glocal approach.
ABOUT THIS BOOK | TOC
