Skip to main content
SpringerLink
Log in
Book cover

Robust and Fault-Tolerant Control pp 1–7Cite as

Introduction

  • Chapter
  • First Online:

  • 571 Accesses

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 197))

Abstract

Chapter constitutes a brief introduction to the control algorithms discussed in the book. The first section aims in presenting the scope of the book which is the application of artificial neural networks to the synthesis of robust and fault tolerant control. The second section describes the content of subsequent chapters.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Åström, K.J., Kumar, P.R.: Control: a perspective. Automatica 50, 3–43 (2014)

    Article  MathSciNet  Google Scholar 

  2. Camacho, E.F., Bordóns, C.: Model Predictive Control, 2nd edn. Springer, London (2007)

    Book  Google Scholar 

  3. Chen, J., Patton, R.J.: Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer, Berlin (1999)

    Book  Google Scholar 

  4. Chen, Y., Wen, C.: Iterative Learning Control. Convergence, Robustness, Applications. Lecture Notes in Control and Information Sciences, vol. 248. Springer, London (1999)

    Google Scholar 

  5. Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks. From Fundamentals to Advanced Theory. Wiley, New Jersey (2003)

    Book  Google Scholar 

  6. Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)

    Google Scholar 

  7. He, N., Shi, D., Forbes, M., Backstörm, J., Chen, T.: Robust tuning for machine-directional predictive control of MIMO paper-making processes. Control Eng. Pract. 55, 1–12 (2016)

    Article  Google Scholar 

  8. Isermann, R.: Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer, New York (2006)

    Google Scholar 

  9. Janakiraman, V., Nguyen, X., Assanis, D.: An ELM based predictive control method for HCCI engines. Eng. Appl. Artif. Intell. 48, 106–118 (2016)

    Article  Google Scholar 

  10. Joosten, D.A., Maciejowski, J.: MPC design for fault-tolerant flight control purposes based upon an existing output feedback controller. In: Proceedings of 7th International Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2009 Barcelona, Spain, 30th June–3rd July 2009. CD-ROM

    Google Scholar 

  11. Korbicz, J., Kościelny, J., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, Berlin (2004)

    Google Scholar 

  12. Ławryńczuk, M.: Computationally Efficient Model Predictive Control Algorithms. A Neural Network Approach. Studies in Systems, Decision and Control, vol. 3. Springer, Switzerland (2014)

    Google Scholar 

  13. Li, S., De Schutter, B., Wang, L., Gao, Z.: Robust model predictive control for train regulation in underground railway transportation. IEEE Trans. Control Syst. Technol. 24, 1075–1083 (2016)

    Article  Google Scholar 

  14. Maciejowski, J.: Predictive Control with Constraints. Prentice-Hall, Harlow (2002)

    MATH  Google Scholar 

  15. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: stability and optimality. Automatica 36, 789–814 (2000)

    Article  MathSciNet  Google Scholar 

  16. Moore, K.L.: Iterative Learning Control for Deterministic Systems. Advances in Industrial Control. Springer, London (1993)

    Book  Google Scholar 

  17. Morari, M., Lee, J.H.: Model predictive control: past, present and future. Comput. Chem. Eng. 23, 667–682 (1999)

    Article  Google Scholar 

  18. Nandan, A., Imtiaz, S.: Nonlinear model predictive control of managed pressure drilling. ISA Trans. 69, 307–314 (2017)

    Article  Google Scholar 

  19. Nelles, O.: Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2001)

    MATH  Google Scholar 

  20. Nørgaard, M., Ravn, O., Poulsen, N., Hansen, L.: Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)

    Book  Google Scholar 

  21. Patan, K.: Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks. Neural Netw. 21, 59–63 (2008)

    Article  Google Scholar 

  22. Patan, K.: Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes. Lecture Notes in Control and Information Sciences. Springer, Berlin (2008)

    Google Scholar 

  23. Scokaert, P., Clarke, D.W.: Stabilizing properties of constrained predictive control. IEE Proc. Control Theory Appl. 141(5), 295–304 (1994)

    Article  Google Scholar 

  24. Skogestad, S., Postlethwaite, I.: Multivariable Feedback Control. Analysis and Design, 2nd edn. Wiley, New York (2005)

    MATH  Google Scholar 

  25. Sridhar, A., Govindarajan, A., Rhinehart, R.R.: Demonstration of leapfrogging for implementing nonlinear model predictive control on a heat exchanger. ISA Trans. 60, 218–227 (2016)

    Article  Google Scholar 

  26. Tatjewski, P.: Advanced Control of Industrial Processes. Springer, London (2007)

    MATH  Google Scholar 

  27. Tornil-Sin, S., Ocampo-Martinez, C., Puig, V., Escobet, T.: Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction. Eng. Appl. Artif. Intell. 25(1), 1–10 (2012)

    Article  Google Scholar 

  28. Verron, S., Tiplica, T., Kobi, A.: Fault diagnosis of industrial systems by conditional gaussian network including a distance rejection criterion. Eng. Appl. Artif. Intell. 23(7), 1229–1235 (2010)

    Article  Google Scholar 

  29. Xu, J.X., Tan, Y.: Linear and Nonlinear Iterative Learning Control for Deterministic Systems. Lecture Notes in Control and Information Sciences, vol. 291. Springer, Berlin (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

Authors
  1. Krzysztof Patan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Patan .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Patan, K. (2019). Introduction. In: Robust and Fault-Tolerant Control. Studies in Systems, Decision and Control, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-030-11869-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation