Skip to main content
Log in

A direct-construction approach to multidimensional realization and LFR uncertainty modeling

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

This article proposes a direct-construction realization procedure that simultaneously treats all the involved variables and/or uncertain parameters and directly generates an overall multidimensional (n-D) Roesser model realization or linear fractional representation (LFR) model for a given n-D polynomial or causal rational transfer matrix. It is shown for the first time that the realization problem for an n-D transfer matrix G(z 1, . . . , z n ), which is assumed without loss of generality to be strictly causal and given in the form of G(z 1, . . . , z n )=N r (z 1, . . . , z n )D −1 r (z 1,..., z n ) with D r (0, . . . , 0)=I and N r (0, . . . , 0)  = 0, can be essentially reduced to the construction of an admissible n-D polynomial matrix Ψ for which there exist real matrices A, B, C such that N r (z 1, . . . , z n ) = CZΨ and Ψ D −1 r (z 1, . . . , z n ) = (I − AZ)−1 B with Z being the corresponding variable and/or uncertainty block structure, i.e., \({Z={\rm diag} \{z_1I_{r_1},\ldots,z_nI_{r_n} \}}\) . This important fact reveals a substantial difference between the 1-D and n-D (n ≥  2) realization problems as in the 1-D case Ψ can only be a monomial matrix and never a polynomial one. Necessary and sufficient conditions for Ψ to satisfy the above restrictions are given and algorithms are proposed for the construction of such an admissible n-D polynomial matrix Ψ with low order (for an arbitrary but fixed field of coefficients) and the corresponding realization. Symbolic and numerical examples are presented to illustrate the basic ideas as well as the effectiveness of the proposed procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Beck, C., & D’Andrea, R. (1997). Minimality, controllability and observability for uncertain systems. In Proceedings of ACC (pp. 3130–3135). New Mexico.

  • Belcastro, C. M. (1994). Uncertainty modeling of real parameter variations for robust control applications. PhD Thesis, Drexel University, USA.

  • Bose N.K. (1982) Applied multidimensional systems theory. Van Nostrand Reinhold, New York

    MATH  Google Scholar 

  • Bose N.K., Charoenlarpnopparut C. (1998) Multivariate matrix factorization: New results. In: (eds) In Proceedings of MTNS’98.. Padova, Italy, pp 97–100

    Google Scholar 

  • Buchberger B. (2003). Gröbner bases: An algorithmic method in polynomial ideal theory, Chap. 4. In N. K. Bose (Ed.). Multidimensional systems theory and applications. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Cheng Y., DeMoor B. (1994) A multidimensional realization algorithm for parametric uncertainty modelling and multiparameter margin problems. International Journal of Control 60: 789–807

    Article  MATH  MathSciNet  Google Scholar 

  • Cockburn, J. C. (2000). Multidimensional realizations of systems with parameter uncertainty. In Proceedings of MTNS, Perpignan, France, June 2000, Session si20a, 6 pages.

  • Cockburn J.C., Morton B.G. (1997) Linear fractional representations of uncertain systems. Automatica 33: 1263–1271

    Article  MATH  MathSciNet  Google Scholar 

  • D’Andrea, R., & Khatri, S. (1997). Kalman decomposition of linear fractional transformation representations and minimality. In Proceedings of ACC (pp. 3557–3561). New Mexico.

  • Eising R. (1978) Realization and stabilization of 2-D systems. IEEE Transactions on Automatic Control 23(5): 793–799

    Article  MATH  MathSciNet  Google Scholar 

  • Galkowski K. (2000) State-space realizations of MIMO 2-D discrete linear systems: Elementary operation and variable inversion approach. International Journal of Control 73(3): 242–253

    Article  MATH  MathSciNet  Google Scholar 

  • Galkowski, K. (2001). State-space realization of linear 2-D systems with extensions to the general n-D (n > 2) case. Springer Verlag, LNCIS.

  • Givone D., Roesser R. (1973) Minimization of multidimensional linear iterative circuits. IEEE Transactions on Computer 22: 673–678

    MATH  MathSciNet  Google Scholar 

  • Guiver J.P., Bose N.K. (1982) Polynomial matrix primitive factorization over arbitrary coefficient field and related results. IEEE Transactions on Circuits and Systems: I 29: 649–657

    Article  MATH  MathSciNet  Google Scholar 

  • Guiver J.P., Bose N.K. (2003) Causal and weak causal 2-D filter with applications in stabilization, Chap. 2. In: Bose K.(eds) Multidimensional systems theory and applications. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Hecker, S. (2007a). Generation of low order LFT representations for robust control applications. Fortschrittberichte VDI, series 8, no. 1114. Available at: http://mediatum2.ub.tum.de/doc/601652/601652.pdf.

  • Hecker, S. (2007b). Private correspondence via emails.

  • Hecker S., Varga A. (2004) Generalized LFT-based representation of parametric uncertain models. European Journal of Control 4: 326–337

    Article  MathSciNet  Google Scholar 

  • Hecker S., Varga A. (2006) Symbolic manipulation techniques for low order LFT-based parametric uncertainty modelling. International Journal of Control 79(11): 1485–1494

    Article  MATH  MathSciNet  Google Scholar 

  • Hecker S., Varga A., Magni J.F. (2005) Enhanced LFR-toolbox for MATLAB. Aerospace Science and Technology 9: 173–180

    Article  Google Scholar 

  • Isidori A., Morse A.S. (1986) State–feedback implementation of cascade compensators. Systems & Control Letters 8: 63–68

    Article  MATH  MathSciNet  Google Scholar 

  • Kailath T. (1980) Linear systems. Prentice-Hall, Inc, USA

    MATH  Google Scholar 

  • Kung S.Y., Levy B.C., Morf M., Kailath T. (1977) New results in 2-D systems theory, part II: 2-D state space models: Realization and the notions of controllability, observability and minimality. Proceedings of IEEE 65: 945–961

    Article  Google Scholar 

  • Kurek J.E. (1985) Basic properties of q-dimensional linear digital systems. International Journal of Control 42: 119–128

    Article  MATH  MathSciNet  Google Scholar 

  • Lambrechts, P., & Terlouw, J. (1992). A matlab toolbox for parametric uncertanty modeling. Philips Research Eindhoven and NLR Amsterdam.

  • Lambrechts P., Terlouw J., Bannani S., Steinbuch M. (1993) Parametric uncertainty modeling using LFT’s. In: (eds) In Proceedings of ACC. San Francisco, CA, pp 267–272

    Google Scholar 

  • Lin Z. (1998) Feedback stabilization of MIMO n-D linear systems. Multidimensional Systems and Signal Processing 9(2): 149–172

    Article  MATH  MathSciNet  Google Scholar 

  • Lin, Z., Xu, L., & Anazawa, Y. (2007). Revisiting the absolutely minimal realization for two-dimensional digital filters. Proceedings of ISCAS07, 597–600.

  • Magni, J. F. (2005). “User manual of the linear fractional representation toolbox,” Version 2. Technical Report. France, October 2005 (revised Feb. 2006). http://www.cert.fr/dcsd/idco/perso/Magni/download/lfrt_manual_v20.pdf.

  • Marcos A., Bates D.G., Postlethwaite I. (2007) A symbolic matrix decomposition algorithm for reduced order linear fractional transformation modelling. Automatica 43: 1211–1218

    Article  MATH  MathSciNet  Google Scholar 

  • Morton, B. (1985). New applications of to real-parameter variation problems. In Proceedings CDC (pp. 233-238). Florida.

  • Russell E.L., Power C.P.H., Braatz R.D. (1997) Multidimensional realization of large scale uncertain systems for multivariable stability margin computation. International Journal of Robust and Nonlinear Control 7: 113–125

    Article  MATH  MathSciNet  Google Scholar 

  • Sugie, T., & Kawanishi, M. (1995). μ analysis/synthesis based on exact expression of physical parameter variations and its application. Proceedings of ECC, 159–164.

  • Varga, A., & Looye, G. (1999). Symbolic and numerical software tools for LFT-based low order uncertainty modelling. In Proceedings of IEEE CACSD (pp. 176–181). Hawaii, USA.

  • Varga A., Looye G., Moormann D., Grnbel G. (1998) Automated generation of LFT-based parametric uncertainty descriptions from generic aircraft models. Mathematical and Computer Modelling of Dynamical Systems 4: 249–274

    Article  MATH  Google Scholar 

  • Xu, L., Fan, H., Lin, Z., Xiao, Y., & Anazawa, Y. (2005). A constructive procedure for multidimensional realization and LFR uncertainty modelling. In Proceedings of ISCAS2005 (pp. 2044–2047). Kobe, Japan.

  • Youla D.C., Gnavi G. (1979) Notes on n-dimensional system theory. IEEE Transactions on Circuits and Systems: I 26(2): 105–111

    Article  MATH  MathSciNet  Google Scholar 

  • Zak S.H., Lee E.B., Lu W.S. (1986) Realizations of 2-D filters and time delay systems. IEEE Transactions on Circuits and Systems, CAS 33(12): 1241–1244

    Article  MathSciNet  Google Scholar 

  • Zerz E. (1999) LFT representations of parameterized polynomial systems. IEEE Transactions on Circuits and Systems I 46: 410–416

    Article  MATH  MathSciNet  Google Scholar 

  • Zerz, E. (2000). Topics in Multidimensional Linear Systems Theory. Lecture Notes in Control and Information Sciences 256. London: Springer-Verlag.

  • Zhuo, K., Doyle, J. C., & Glover, K. (1996). Robust and optimal control. Prentice Hall.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, L., Fan, H., Lin, Z. et al. A direct-construction approach to multidimensional realization and LFR uncertainty modeling. Multidim Syst Sign Process 19, 323–359 (2008). https://doi.org/10.1007/s11045-008-0057-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-008-0057-0

Keywords

Navigation