Discretization of Linear Fractional Representations of LPV systems
Roland Tóth,Marco Lovera,Peter S. C. Heuberger,P.M.J. Van den Hof +3 more
- pp 7424-7429
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TLDR
The proposed and existing methods are compared and analyzed in terms of approximation error, considering ideal zero-order hold actuation and sampling, and criteria to choose appropriate sampling times with respect to the investigated methods are presented.Abstract:
Commonly, controllers for Linear Parameter- Varying (LPV) systems are designed in continuous-time using a Linear Fractional Representation (LFR) of the plant. However, the resulting controllers are implemented on digital hardware. Furthermore, discrete-time LPV synthesis approaches require a discrete-time model of the plant which is often derived from continuous-time first-principle models. Existing discretization approaches for LFRs suffer from disadvantages like alternation of dynamics, complexity, etc. To overcome the disadvantages, novel discretization methods are derived. These approaches are compared to existing techniques and analyzed in terms of approximation error, considering ideal zero-order hold actuation and sampling.read more
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Journal ArticleDOI
On the Discretization of Linear Fractional Representations of LPV Systems
TL;DR: The proposed and existing methods are compared and analyzed in terms of approximation error, considering ideal zero-order hold actuation and sampling, and criteria to choose appropriate sampling times with respect to the investigated methods are presented.
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Affine LPV systems: realization theory, input-output equations and relationship with linear switched systems
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References
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Book ChapterDOI
Computer Controlled Systems
TL;DR: The degree of success of a computer control application depends mainly on the effectiveness of the algorithm, which is generally designed from plant specifications and desired performance characteristics.
Book
Modeling and Identification of Linear Parameter-Varying Systems
TL;DR: This book aims to bridge the gap between Linear Parameter-Varying (LPV) modeling and control by investigating fundamental questions of modeling and identification by proposing a unified LPV system theory, based on a behavioral approach, giving a solid basis for an identification theory.
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