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Model-Free Optimal Control: A Critical Analysis

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Big Data Analytics (BDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10721))

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Abstract

In this note, we present a critical analysis of machine learning techniques for applications involving optimal (feedback) control. Specifically, we will focus on the question of using reinforcement learning and other similar techniques in providing provably stable optimal controllers.

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References

  1. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2014)

    MATH  Google Scholar 

  2. Antsaklis, P.J., Michel, A.N.: Linear Systems, vol. 1. Birkhäuser, Boston (2006)

    MATH  Google Scholar 

  3. Aström, K.J., Murray, R.M.: Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, Princeton (2010)

    Google Scholar 

  4. Åström, K.J., Wittenmark, B.: Adaptive Control. Courier Corporation, Mineola (2013)

    Google Scholar 

  5. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming, 1st edn. Athena Scientific, Belmont (1996)

    MATH  Google Scholar 

  6. Bhatia, N.P., Szegö, G.P.: Dynamical Systems: Stability Theory and Applications, vol. 35. Springer, Heidelberg (2006). https://doi.org/10.1007/BFb0080630

    MATH  Google Scholar 

  7. Chellaboina, V., Bhat, S.P., Haddad, W.M.: An invariance principle for nonlinear hybrid and impulsive dynamical systems. Nonlinear Anal. Theory Methods Appl. 53(3), 527–550 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Dullerud, G.E., Paganini, F.: A Course in Robust Control Theory: A Convex Approach, vol. 36. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-3290-0

    MATH  Google Scholar 

  9. Duriez, T., Brunton, S.L., Noack, B.R.: Machine Learning Control-Taming Nonlinear Dynamics and Turbulence. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-40624-4

    Book  MATH  Google Scholar 

  10. Dydek, Z.T., Annaswamy, A.M., Lavretsky, E.: Adaptive control and the NASA X-15-3 flight revisited. IEEE Control Syst. 30(3), 32–48 (2010)

    Article  MathSciNet  Google Scholar 

  11. Fogel, D.B.: System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham Heights (1991)

    Google Scholar 

  12. Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control, vol. 13. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-6577-9

    MATH  Google Scholar 

  13. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  14. Haddad, W.M., Chellaboina, V.: Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach. Princeton University Press, Princeton (2008)

    MATH  Google Scholar 

  15. Haddad, W.M., Chellaboina, V., Nersesov, S.G.: Impulsive and Hybrid Dynamical Systems. Princeton Series in Applied Mathematics (2006)

    Google Scholar 

  16. Hovakimyan, N., Cao, C.: \(\cal{L}\)1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation. SIAM, Philadelphia (2010)

    Google Scholar 

  17. Ioannou, P.A., Sun, J.: Robust Adaptive Control, vol. 1. PTR Prentice-Hall, Upper Saddle River (1996)

    MATH  Google Scholar 

  18. Jenkins, D.R.: Hypersonics before the shuttle: A concise history of the X-15 research airplane (2000)

    Google Scholar 

  19. Lewis, F.L., Liu, D.: Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, vol. 17. Wiley, New York (2013)

    Google Scholar 

  20. Ljung, L.: System identification. In: Procházka, A., Uhlíř, J., Rayner, P.W.J., Kingsbury, N.G. (eds.) Signal Analysis and Prediction. Applied and Numerical Harmonic Analysis, pp. 163–173. Springer, Heidelberg (1998). https://doi.org/10.1007/978-1-4612-1768-8_11

    Chapter  Google Scholar 

  21. Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning: An Artificial Intelligence Approach. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  22. Narendra, K.S., Annaswamy, A.M.: Stable Adaptive Systems. Courier Corporation, Mineola (2012)

    MATH  Google Scholar 

  23. Ng, A.Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., Liang, E.: Autonomous inverted helicopter flight via reinforcement learning. In: Ang, M.H., Khatib, O. (eds.) Experimental Robotics IX. Springer Tracts in Advanced Robotics, vol. 21, pp. 363–372. Springer, Heidelberg (2006). https://doi.org/10.1007/11552246_35

    Chapter  Google Scholar 

  24. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, vol. 1. MIT press, Cambridge (1998)

    Google Scholar 

  25. Sutton, R.S., Barto, A.G., Williams, R.J.: Reinforcement learning is direct adaptive optimal control. IEEE Control Syst. 12(2), 19–22 (1992)

    Article  Google Scholar 

  26. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)

    Google Scholar 

  27. Zhou, K., Doyle, J.C.: Essentials of Robust Control, vol. 104. Prentice hall, Upper Saddle River (1998)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Mahindra École Centrale, Hyderabad, India

    Vijaysekhar Chellaboina

Authors
  1. Vijaysekhar Chellaboina
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Correspondence to Vijaysekhar Chellaboina .

Editor information

Editors and Affiliations

  1. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

  2. Rajiv Gandhi Education City, Sonepat, India

    Ashish Sureka

  3. University of Texas at Arlington, Arlington, Texas, USA

    Sharma Chakravarthy

  4. University of Aizu, Aizu-Wakamatsu, Japan

    Subhash Bhalla

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Chellaboina, V. (2017). Model-Free Optimal Control: A Critical Analysis. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-72413-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72412-6

  • Online ISBN: 978-3-319-72413-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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