Skip to main content
SpringerLink
Log in

Learning Automata Applied to Planning Control

  • Chapter
  • First Online:
Engineering Applications of Soft Computing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 129))

  • 585 Accesses

Abstract

Planning Control uses information regarding a problem and its environment to decide whether one plan is better than other in order to reach a required control objective. An interesting alternative for planning control is model predictive control (MPC) and the receding horizon control. MPC is the planning approach that has recently found a wide acceptance for industrial applications.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Liu GP (2001) Nonlinear identification and control: a neural network approach. Springer, Berlin

    Book  MATH  Google Scholar 

  2. Fleming PJ, Purshouse RC (2002) Evolutionary algorithms in control systems engineering: a survey. Control Eng Pract 10(2002):1223–1241

    Article  Google Scholar 

  3. Ying-Pin C, Low C, Shih-Yu H (2009) Integrated feasible direction method and genetic algorithm for optimal planning of harmonic filters with uncertainty conditions. Expert Syst Appl 36:3946–3955

    Article  Google Scholar 

  4. Huang L (2009) Velocity planning for a mobile robot to track a moving target—a potential field approach. Robot Auton Syst 57:55–63

    Article  Google Scholar 

  5. Chauvin J, Corde G, Petit N, Rouchon P (2008) Motion planning for experimental airpath control of a diesel homogeneous charge-compression ignition engine. Control Eng Pract 16(9):1081–1091

    Article  Google Scholar 

  6. Son C (2006) Comparison of intelligent control planning algorithms for robot’s part micro-assembly task. Eng Appl Artif Intell 19(1):41–52

    Article  Google Scholar 

  7. Seow K, Sim K (2008) Collaborative assignment using belief-desire-intention agent modeling and negotiation with speedup strategies. Inf Sci 178(2):1110–1132

    Google Scholar 

  8. Camacho E, Bordons C (2008) Model predictive control (advanced textbooks in control and signal processing). Springer, Berlin

    Google Scholar 

  9. Garcia CE, Prett DM, Morari M (1989) Model predictive control: theory and practice—a survey. Automatica 25:335

    Article  MATH  Google Scholar 

  10. Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: stability and optimality. Automatica 36(6):789

    Article  MathSciNet  MATH  Google Scholar 

  11. Camacho EF, Bordons C (2007) Nonlinear model predictive control: an introductory review. Assessment and future directions of nonlinear model predictive control. Springer, Berlin

    Book  MATH  Google Scholar 

  12. Nagy ZK, Mahn B, Franke R, Allgower F (2007) Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor. Control Eng Pract 15(7):839–850

    Article  Google Scholar 

  13. Cueli R, Bordons C (2008) Iterative nonlinear model predictive control. Stability, robustness and applications. Control Eng Pract 16:1023–1034

    Article  Google Scholar 

  14. Canale M, Fagiano L, Milanese M (2009) Set membership approximation theory for fast implementation of model predictive control laws. Automatica 45:45–54

    Article  MathSciNet  MATH  Google Scholar 

  15. Potočnik B, Mušič G, Škrjanc I, Zupančič B (2008) Model-based predictive control of hybrid systems: a probabilistic neural-network approach to real-time control. J Intell Robot Syst 51:45–63

    Article  Google Scholar 

  16. Seyed-Hamid Z (2008) Learning automata based classifier. Pattern Recognit Lett 29:40–48

    Article  Google Scholar 

  17. Zeng X, Zhou J, Vasseur C (2000) A strategy for controlling non-linear systems using a learning automaton. Automatica 36:1517–1524

    Article  MathSciNet  MATH  Google Scholar 

  18. Howell M, Gordon T (2001) Continuous action reinforcement learning automata and their application to adaptive digital filter design. Eng Appl Artif Intell 14:549–561

    Article  Google Scholar 

  19. Wu QH (1995) Learning coordinated control of power systems using inter-connected learning automata. Int J Electr Power Energy Syst 17:91–99

    Article  Google Scholar 

  20. Thathachar MAL, Sastry PS (2002) Varieties of learning automata: an overview. IEEE Trans Syst Man Cybern Part B Cybern 32:711–722

    Article  Google Scholar 

  21. Zeng X, Liu Z (2005) A learning automaton based algorithm for optimization of continuous complex function. Inf Sci 174:165–175

    Article  MATH  Google Scholar 

  22. Beygi H, Meybodi MR (2006) A new action-set learning automaton for function optimization. Int J Franklin Inst 343:27–47

    Article  MathSciNet  MATH  Google Scholar 

  23. Howell MN, Frost GP, Gordon TJ, Wu QH (1997) Continuous action reinforcement learning applied to vehicle suspension control. Mechatronics 7(3):263–276

    Article  Google Scholar 

  24. Kashki M, Lofty Y, Abdel-Magid, Abido MA (2008) Advanced intelligent computing theories and applications. With aspects of artificial intelligence. In: Huang et al (ed) A reinforcement learning automata optimization approach for optimum tuning of PID controller in AVR system. Springer, Berlin, pp 684–692

    Google Scholar 

  25. Kelley CT (2000) Iterative methods for optimization, SIAM frontiers in applied mathematics, no 18. ISBN 0-89871-433-8

    Google Scholar 

  26. Tsetlin ML (1973) Automaton theory and modeling of biological systems. Academic Press, New York

    MATH  Google Scholar 

  27. Narendra KS, Thathachar MAL (1989) Learning automata: an introduction. Prentice-Hall, London

    MATH  Google Scholar 

  28. Najim K, Poznyak AS (1994) Learning automata—theory and applications. Pergamon Press, Oxford

    MATH  Google Scholar 

  29. Thathachar MAL, Sastry S (2004) Techniques for online stochastic optimization. Springer, New York

    Google Scholar 

  30. Press W, Flannery B, Teukolsky, Vetterling W (1992) Numerical recipes in C: the art of scientific computing, 2 edn. Cambridge University Press, Cambridge

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Margarita-Arimatea Díaz-Cortés & Raúl Rojas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas

Authors
  1. Margarita-Arimatea Díaz-Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raúl Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Margarita-Arimatea Díaz-Cortés .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Díaz-Cortés, MA., Cuevas, E., Rojas, R. (2017). Learning Automata Applied to Planning Control. In: Engineering Applications of Soft Computing. Intelligent Systems Reference Library, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-57813-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57813-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57812-5

  • Online ISBN: 978-3-319-57813-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation