Practical Aspects

  • Ioan Doré Landau
  • Rogelio Lozano
  • Mohammed M’Saad
  • Alireza Karimi
Part of the Communications and Control Engineering book series (CCE)


The chapter examines the impact of practical aspects upon the design and implementation of the adaptive control systems. This involves: choice of the sampling frequency and anti-aliasing filters, taking into account the digital-to-analog converter, handling the possible actuator saturation, taking in account the computational delay, choice of the performance, numerically safe implementation of the parameter adaptation algorithms, initialization of the adaptive control scheme, and supervision.


Adaptive Control Actuator Saturation Digital Controller Adaptive Control Scheme Adaptive Control System 
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  1. Åström KJ, Wittenmark B (1984) Computer controlled systems, theory and design. Prentice-Hall, Englewood Cliffs Google Scholar
  2. Åström KJ, Hagander P, Sternby J (1984) Zeros of sampled systems. Automatica 20:31–38 MATHCrossRefGoogle Scholar
  3. Athans M, Castanon D, Dunn K, Greene C, Lee W, Sandell N Jr, Willsky A (2002) The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method—Part I: Equilibrium flight. IEEE Trans Autom Control 22(5):768–780 CrossRefGoogle Scholar
  4. Bierman GJ (1977) Factorization methods for discrete sequential estimation. Academic Press, New York MATHGoogle Scholar
  5. Böling JM, Seborg DE, Hespanha JP (2007) Multi-model adaptive control of a simulated PH neutralization process. Control Eng Pract 15(6):663–672 CrossRefGoogle Scholar
  6. Clary JP, Franklin GF (1985) A variable dimension self-tuning controller. In: Proc ACC conf, Boston, USA Google Scholar
  7. Franklin GF, Powell JD, Workman M (1990) Digital control of dynamic systems, 2nd edn. Addison Wesley, Reading MATHGoogle Scholar
  8. Karimi A, Landau ID (2000) Robust adaptive control of a flexible transmission system using multiple models. IEEE Trans Control Syst Technol 8(2):321–331 MathSciNetCrossRefGoogle Scholar
  9. Karimi A, Landau ID, Motee N (2001) Effects of the design parameters of multimodel adaptive control on the performance of a flexible transmission system. Int J Adapt Control Signal Process 15(3):335–352 MATHCrossRefGoogle Scholar
  10. Landau ID (1990b) System identification and control design. Prentice Hall, Englewood Cliffs MATHGoogle Scholar
  11. Landau ID (1993b) Identification et Commande des Systèmes, 2nd edn. Série Automatique. Hermès, Paris MATHGoogle Scholar
  12. Landau ID, Zito G (2005) Digital control systems—design, identification and implementation. Springer, London Google Scholar
  13. Li XR, Bar-Shalom Y (2002) Design of an interacting multiple model algorithm for air traffic control tracking. IEEE Trans Control Syst Technol 1(3):186–194 CrossRefGoogle Scholar
  14. M’Saad M (1994) Un logiciel pour la commande avancée des procédés industriels. In: Proc 2AO conf, Noisy-le-Grand, France Google Scholar
  15. M’Saad M, Hejda I (1994) Partial state reference model (adaptive) control of a benchmark example. Automatica 30(4):605–614 MathSciNetMATHCrossRefGoogle Scholar
  16. M’Saad M, Duque M, Irving E (1987) Thermal process robust adaptive control: an experimental evaluation. In: Proc of the 10th IFAC World congress, Munich, Germany Google Scholar
  17. M’Saad M, Landau ID, Duque M (1989) Example applications of the partial state reference model adaptive control design technique. Int J Adapt Control Signal Process 3(2):155–165 CrossRefGoogle Scholar
  18. M’Saad M, Landau ID, Samaan M (1990) Further evaluation of the partial state reference model adaptive control design. Int J Adapt Control Signal Process 4(2):133–146 CrossRefGoogle Scholar
  19. M’Saad M, Dugard L, Hammad S (1993a) A suitable generalized predictive adaptive controller case study: control of a flexible arm. Automatica 29(3):589–608 MathSciNetMATHCrossRefGoogle Scholar
  20. Najim K, Najim M, Youlal H (1982) Self-tuning control of an industrial phosphate dry process. Optim Control Appl Methods 3:435–442 Google Scholar
  21. Najim K, Hodouin D, Desbiens A (1994) Adaptive control: state of the art and an application to a grinding circuit. Powder Technol 82:56–68 Google Scholar
  22. Press W, Flamery B, Tenkolsky S, Veterling W (1988) Numerical recipes in C. Cambridge University Press, Cambridge MATHGoogle Scholar
  23. Queinnec I, Dahhou B, M’Saad M (1992) An adaptive control of fermentation processes. Int J Adapt Control Signal Process 6:521–536 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Ioan Doré Landau
    • 1
  • Rogelio Lozano
    • 2
  • Mohammed M’Saad
    • 3
  • Alireza Karimi
    • 4
  1. 1.Département d’AutomatiqueGIPSA-LAB (CNRS/INPG/UJF)St. Martin d’HeresFrance
  2. 2.UMR-CNRS 6599, Centre de Recherche de Royalieu, Heuristique et Diagnostic des Systèmes ComplexesUniversité de Technologie de CompiègneCompiègneFrance
  3. 3.Centre de Recherche (ENSICAEN), Laboratoire GREYCÉcole Nationale Supérieure d’Ingénieurs de CaenCaen CedexFrance
  4. 4.Laboratoire d’AutomatiqueÉcole Polytechnique Fédérale de LausanneLaussanneSwitzerland

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