Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Experiment Design and Identification for Control

  • Håkan HjalmarssonEmail author
Living reference work entry

Latest version View entry history

DOI: https://doi.org/10.1007/978-1-4471-5102-9_103-2

Abstract

The experimental conditions have a major impact on the estimation result. Therefore, the available degrees of freedom in this respect that are at the disposal to the user should be used wisely. This entry provides the fundamentals for the available techniques. We also briefly discuss the particulars of identification for model-based control, one of the main applications of system identification.

Keywords

Adaptive experiment design Application-oriented experiment design Cramér-Rao lower bound Crest factor Experiment design Fisher information matrix Identification for control Least-costly identification Multisine Pseudorandom binary signal (PRBS) Robust experiment design 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by the European Research Council under the advanced grant LEARN, contract 267381, and by the Swedish Research Council, contracts 2016-06079.

Bibliography

  1. Agüero JC, Goodwin GC (2007) Choosing between open and closed loop experiments in linear system identification. IEEE Trans Autom Control 52(8):1475–1480MathSciNetCrossRefGoogle Scholar
  2. Bombois X, Scorletti G, Gevers M, Van den Hof PMJ, Hildebrand R (2006) Least costly identification experiment for control. Automatica 42(10):1651–1662CrossRefGoogle Scholar
  3. Fedorov VV (1972) Theory of optimal experiments. Probability and mathematical statistics, vol 12. Academic, New YorkGoogle Scholar
  4. Forssell U, Ljung L (1999) Closed-loop identification revisited. Automatica 35:1215–1241MathSciNetCrossRefGoogle Scholar
  5. Fujimoto Y, Maruta I, Sugie T (2018) Input design for kernel-based system identification from the viewpoint of frequency response. IEEE Trans Autom Control 63(9):3075–3082MathSciNetCrossRefGoogle Scholar
  6. Gevers M (2005) Identification for control: from the early achievements to the revival of experiment design. Eur J Control 11(4–5):335–352. Semi-plenary lecture at IEEE conference on decision and control – European control conferenceMathSciNetCrossRefGoogle Scholar
  7. Gevers M, Ljung L (1986) Optimal experiment designs with respect to the intended model application. Automatica 22(5):543–554MathSciNetCrossRefGoogle Scholar
  8. Gevers M, Bombois X, Codrons B, Scorletti G, Anderson BDO (2003) Model validation for control and controller validation in a prediction error identification framework – part I: theory. Automatica 39(3):403–445MathSciNetCrossRefGoogle Scholar
  9. Goodwin GC, Payne RL (1977) Dynamic system identification: experiment design and data analysis. Academic, New YorkzbMATHGoogle Scholar
  10. Hjalmarsson H (2005) From experiment design to closed loop control. Automatica 41(3):393–438MathSciNetCrossRefGoogle Scholar
  11. Hjalmarsson H (2009) System identification of complex and structured systems. Eur J Control 15(4):275–310. Plenary address. European control conferenceMathSciNetCrossRefGoogle Scholar
  12. Jansson H, Hjalmarsson H (2005) Input design via LMIs admitting frequency-wise model specifications in confidence regions. IEEE Trans Autom Control 50(10):1534–1549MathSciNetCrossRefGoogle Scholar
  13. Larsson CA, Hjalmarsson H, Rojas CR, Bombois X, Mesbah A, Modén P-E (2013) Model predictive control with integrated experiment design for output error systems. In: European control conference, ZurichCrossRefGoogle Scholar
  14. Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  15. Manchester IR (2010) Input design for system identification via convex relaxation. In: 49th IEEE conference on decision and control, Atlanta, pp 2041–2046Google Scholar
  16. Pintelon R, Schoukens J (2012) System identification: a frequency domain approach, 2nd edn. Wiley/IEEE, Hoboken/PiscatawayCrossRefGoogle Scholar
  17. Pronzato L (2008) Optimal experimental design and some related control problems. Automatica 44(2):303–325MathSciNetCrossRefGoogle Scholar
  18. Rathouský J, Havlena V (2013) MPC-based appro- ximate dual controller by information matrix maximization. Int J Adapt Control Signal Process 27(11):974–999CrossRefGoogle Scholar
  19. Raynaud HF, Pronzato L, Walter E (2000) Robust identification and control based on ellipsoidal parametric uncertainty descriptions. Eur J Control 6(3):245–255MathSciNetCrossRefGoogle Scholar
  20. Rivera DE, Lee H, Mittelmann HD, Braun MW (2009) Constrained multisine input signals for plant-friendly identification of chemical process systems. J Process Control 19(4):623–635CrossRefGoogle Scholar
  21. Rojas CR, Agüero JC, Welsh JS, Goodwin GC (2008) On the equivalence of least costly and traditional experiment design for control. Automatica 44(11):2706–2715MathSciNetCrossRefGoogle Scholar
  22. Rojas CR, Welsh JS, Agüero JC (2009) Fundamental limitations on the variance of parametric models. IEEE Trans Autom Control 54(5):1077–1081MathSciNetCrossRefGoogle Scholar
  23. Zarrop M (1979) Optimal experiment design for dynamic system identification. Lecture notes in control and information sciences, vol 21. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer Science, Centre for Advanced BioproductionKTH Royal Institute of TechnologyStockholmSweden

Section editors and affiliations

  • Lennart Ljung
    • 1
  1. 1.Division of Automatic Control, Department of Electrical EngineeringLinköping UniversityLinköpingSweden