Advertisement

Introduction

  • Krzysztof PatanEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (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.

References

  1. 1.
    Åström, K.J., Kumar, P.R.: Control: a perspective. Automatica 50, 3–43 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Camacho, E.F., Bordóns, C.: Model Predictive Control, 2nd edn. Springer, London (2007)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Patton, R.J.: Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer, Berlin (1999)CrossRefGoogle Scholar
  4. 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. 5.
    Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks. From Fundamentals to Advanced Theory. Wiley, New Jersey (2003)CrossRefGoogle Scholar
  6. 6.
    Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Isermann, R.: Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer, New York (2006)Google Scholar
  9. 9.
    Janakiraman, V., Nguyen, X., Assanis, D.: An ELM based predictive control method for HCCI engines. Eng. Appl. Artif. Intell. 48, 106–118 (2016)CrossRefGoogle Scholar
  10. 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-ROMGoogle Scholar
  11. 11.
    Korbicz, J., Kościelny, J., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, Berlin (2004)Google Scholar
  12. 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. 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)CrossRefGoogle Scholar
  14. 14.
    Maciejowski, J.: Predictive Control with Constraints. Prentice-Hall, Harlow (2002)zbMATHGoogle Scholar
  15. 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)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Moore, K.L.: Iterative Learning Control for Deterministic Systems. Advances in Industrial Control. Springer, London (1993)CrossRefGoogle Scholar
  17. 17.
    Morari, M., Lee, J.H.: Model predictive control: past, present and future. Comput. Chem. Eng. 23, 667–682 (1999)CrossRefGoogle Scholar
  18. 18.
    Nandan, A., Imtiaz, S.: Nonlinear model predictive control of managed pressure drilling. ISA Trans. 69, 307–314 (2017)CrossRefGoogle Scholar
  19. 19.
    Nelles, O.: Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2001)zbMATHGoogle Scholar
  20. 20.
    Nørgaard, M., Ravn, O., Poulsen, N., Hansen, L.: Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)CrossRefGoogle Scholar
  21. 21.
    Patan, K.: Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks. Neural Netw. 21, 59–63 (2008)CrossRefGoogle Scholar
  22. 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. 23.
    Scokaert, P., Clarke, D.W.: Stabilizing properties of constrained predictive control. IEE Proc. Control Theory Appl. 141(5), 295–304 (1994)CrossRefGoogle Scholar
  24. 24.
    Skogestad, S., Postlethwaite, I.: Multivariable Feedback Control. Analysis and Design, 2nd edn. Wiley, New York (2005)zbMATHGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 26.
    Tatjewski, P.: Advanced Control of Industrial Processes. Springer, London (2007)zbMATHGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

Personalised recommendations