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
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
Citations
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Proceedings ArticleDOI
Discrete inversion based FDI for sampled LPV systems
TL;DR: The paper investigates the design problem for detection and isolation of faults in linear parameter varying (LPV) systems by means of dynamic inversion where the system matrix depends affinely from the parameters.
Proceedings ArticleDOI
Discretization of linear parameter varying systems in the linear fractional representation with constant and with parameter dependent sampling rates
TL;DR: The discretization of continuous Linear Parameter Varying systems (LPV) both for constant and for parameter dependent sampling rates are studied in this paper, based on the Linear Fractional Transformation (LFT).
Proceedings ArticleDOI
Controle LFR Discreto de Quadrirotores usando o Framework ROS
TL;DR: In this article, the authors present the development of the discrete version of the Linear Fractional Representation (LFR) control method for nonlinear systems, based on Linear Matrix Inequalities (LMIs).
Proceedings ArticleDOI
Discretization of linear parameter varying systems in the LFT representation with parameter dependent sampling rates
TL;DR: In this work two new procedures are proposed to discretize linear parameter varying (LPV) models in the linear fractional transformation LFT representation, using a Taylor series approximation to obtain multivariable discrete affine models for parameter dependent sampling rates of continuous LPV systems for each sampling period.
Journal ArticleDOI
Linear Parameter-Varying state feedback synthesis with hierarchical performance requirements
TL;DR: In this paper, the problem of LPV state-feedback synthesis is considered and an approach allowing for the specification and optimisation of different performance levels for suitably chosen subranges of the parameters and their rates is proposed.
References
More filters
Journal ArticleDOI
I and i
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Book
Robust and Optimal Control
TL;DR: This paper reviewed the history of the relationship between robust control and optimal control and H-infinity theory and concluded that robust control has become thoroughly mainstream, and robust control methods permeate robust control theory.
Book
Solving Ordinary Differential Equations II: Stiff and Differential - Algebraic Problems
Ernst Hairer,Gerhard Wanner +1 more
TL;DR: In this paper, the authors present the solution of stiff differential equations and differential-algebraic systems (differential equations with constraints) and discuss their application in physics, chemistry, biology, control engineering, electrical network analysis, and computer programs.
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