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Directions in Mathematical Systems Theory and Optimization

  • Anders Rantzer
  • Christopher I. Byrnes

Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 286)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Karl Johan Åström, Bo Bernhardsson
    Pages 1-13
  3. H. T. Banks, K. M. Furati, K. Ito, N. S. Luke, C. J. Smith
    Pages 15-26
  4. Vivek S. Borkar, Sanjoy K. Mitter
    Pages 41-49
  5. Christopher I. Byrnes, David S. Gilliam, Alberto Isidori, Yutaka Ikeda, Lorenzo Marconi
    Pages 51-70
  6. Peter E. Caines, R. Deardon, H. P. Wynn
    Pages 71-84
  7. Alessandro Chiuso, Giorgio Picci
    Pages 85-126
  8. Harry Dym
    Pages 127-133
  9. László Gerencsér, György Michaletzky
    Pages 141-157
  10. Xiaoming Hu, Ulf Jönsson, Clyde F. Martin
    Pages 159-172
  11. Arthur J. Krener
    Pages 173-182
  12. Lennart Ljung
    Pages 203-215
  13. Michele Pavon
    Pages 227-238
  14. Eric Pichon, Allen Tannenbaum, Guillermo Sapiro
    Pages 239-247
  15. Boris T. Polyak
    Pages 249-260
  16. Stephen Prajna, Anders Rantzer
    Pages 261-274
  17. Olof J. Staffans
    Pages 275-292
  18. Héctor J. Sussmann
    Pages 293-316
  19. Tzyh-Jong Tarn, Mingjun Zhang, Andrea Serrani
    Pages 317-331
  20. Jan C. Willems, Paolo Rapisarda
    Pages 345-357
  21. Back Matter
    Pages 383-389

About these proceedings

Introduction

For more than three decades, Anders Lindquist has delivered fundamental cont- butions to the ?elds of systems, signals and control. Throughout this period, four themes can perhaps characterize his interests: Modeling, estimation and ?ltering, feedback and robust control. His contributions to modeling include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identi?cation. His contributions to ?ltering and estimation include the development of fast ?ltering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. His further research on the phase portrait of this dynamical system gave a better understanding of when the Kalman ?lter will converge, answering an open question raised by Kalman. While still a student he established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. He continued his interest in feedback control by deriving optimal and robust control feedback laws for suppressing the effects of harmonic disturbances. Moreover, his recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.

Keywords

Analysis Signal Transformation algorithm algorithms computer-aided design (CAD) model modeling operator optimal control optimization system system identification systems theory

Editors and affiliations

  • Anders Rantzer
    • 1
  • Christopher I. Byrnes
    • 2
  1. 1.Department of Automatic ControlLund Institute of TechnologyLundSweden
  2. 2.School of Engineering and Applied ScienceWashington UniversitySt. LouisUSA

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-36106-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-00065-5
  • Online ISBN 978-3-540-36106-0
  • Series Print ISSN 0170-8643
  • Buy this book on publisher's site
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