LFR-toolbox

Enhanced LFR-toolbox for Matlab. We describe recent developments and enhancements of the LFR-toolbox for Matlab for building LFT-based uncertainty models and for LFT-based gain scheduling. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-representations and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust Fortran implementations of order reduction tools via mex-function interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-representations with significantly lower orders.


References in zbMATH (referenced in 23 articles , 1 standard article )

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  1. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Computational method for estimating the domain of attraction of discrete-time uncertain rational systems (2019)
  2. Yan, Shi; Xu, Li; Zhang, Yining; Cai, Yunze; Zhao, Dongdong: Order evaluation to new elementary operation approach for MIMO multidimensional systems (2019)
  3. Zhao, Dongdong; Galkowski, Krzysztof; Sulikowski, Bartlomiej; Xu, Li: 3-D modelling of rectangular circuits as the particular class of spatially interconnected systems on the plane (2019)
  4. Zhao, Dongdong; Yan, Shi; Matsushita, Shinya; Xu, Li: Common eigenvector approach to exact order reduction for Roesser state-space models of multidimensional systems (2019)
  5. Polcz, Péter; Péni, Tamás; Szederkényi, Gábor: Improved algorithm for computing the domain of attraction of rational nonlinear systems (2018)
  6. Zhao, Dongdong; Yan, Shi; Xu, Li: Eigenvalue trim approach to exact order reduction for Roesser state-space model of multidimensional systems (2018)
  7. Nguyen, Thi Loan; Xu, Li; Lin, Zhiping; Tay, David B. H.: On minimal realizations of first-degree 3D systems with separable denominators (2017)
  8. Rosa, Paulo; Simão, Tiago; Silvestre, Carlos; Lemos, João M.: Fault-tolerant control of an air heating fan using set-valued observers: an experimental evaluation (2016)
  9. Henry, D.; Cieslak, J.; Zolghadri, A.; Efimov, D.: (H_\infty/H_-) LPV solutions for fault detection of aircraft actuator faults: bridging the gap between theory and practice (2015)
  10. Henry, D.: Structured fault detection filters for LPV systems modeled in an LFR manner (2012)
  11. Xu, Li; Fan, Huijin; Lin, Zhiping; Xiao, Yegui: Coefficient-dependent direct-construction approach to realization of multidimensional systems in Roesser model (2011)
  12. Henrion, Didier: Detecting rigid convexity of bivariate polynomials (2010)
  13. Tóth, Roland: Modeling and identification of linear parameter-varying systems (2010)
  14. Xu, Li; Yan, Shi: A new elementary operation approach to multidimensional realization and LFR uncertainty modeling: the SISO case (2010)
  15. Yan, Shi; Shiratori, Natsuko; Xu, Li: Simple state-space formulations of 2-D frequency transformation and double bilinear transformation (2010)
  16. Roos, C.; Biannic, J.-M.: A convex characterization of dynamically-constrained anti-windup controllers (2008)
  17. Xu, Li; Fan, Huijin; Lin, Zhiping; Bose, N. K.: A direct-construction approach to multidimensional realization and LFR uncertainty modeling (2008)
  18. Ferreres, Gilles; Roos, Clément: Robust feedforward design in the presence of LTI/LTV uncertainties (2007)
  19. Biannic, Jean-Marc; Roos, C.; Knauf, A.: Design and robustness analysis of fighter aircraft flight control laws (2006)
  20. Hecker, S.; Varga, A.: Symbolic manipulation techniques for low order LFT-based parametric uncertainty modelling (2006)

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