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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 398))

Abstract

In this paper, we study the sample complexity of probabilistic methods for control of uncertain systems. In particular, we show the role of the binomial distribution for some problems involving analysis and design of robust controllers with finite families. We also address the particular case in which the design problem can be formulated as an uncertain convex optimization problem. The results of the paper provide simple explicit sample bounds to guarantee that the obtained solutions meet some pre-specified probabilistic specifications.

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References

  1. Alamo, T., Tempo, R., Camacho, E.F.: Randomized strategies for probabilistic solutions of uncertain feasibility and optimization problems. IEEE Transactions on Automatic Control 54(11), 2545–2559 (2009)

    Article  MathSciNet  Google Scholar 

  2. Bai, E., Cho, H., Tempo, R., Ye, Y.: Optimization with few violated constraints for linear bounded error parameter estimation. IEEE Transactions on Automatic Control 47(7), 1067–1077 (2002)

    Article  MathSciNet  Google Scholar 

  3. Calafiore, G., Campi, M.C.: The scenario approach to robust control design. IEEE Transactions on Automatic Control 51(5), 742–753 (2006)

    Article  MathSciNet  Google Scholar 

  4. Campi, M.C., Garatti, S.: The exact feasibility of randomized solutions of robust convex programs. SIAM Journal on Optimization 19, 1211–1230 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  5. Chernoff, H.: A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Annals of Mathematical Statistics 23, 493–507 (1952)

    Article  MATH  MathSciNet  Google Scholar 

  6. Fujisaki, Y., Kozawa, Y.: Probabilistic robust controller design: probable near minimax value and randomized algorithms. In: Calafiore, G., Dabbene, F. (eds.) Probabilistic and Randomized Methods for Design under Uncertainty. Springer, London (2006)

    Google Scholar 

  7. Koltchinskii, V., Abdallah, C.T., Ariola, M., Dorato, P., Panchenko, D.: Improved sample complexity estimates for statistical learning control of uncertain systems. IEEE Transactions on Automatic Control 45(12), 2383–2388 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  8. Luedtke, J., Ahmed, S.: A sample approximation approach for optimization with probabilistic constraints. SIAM Journal on Optimization 19(2), 674–699 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  9. Okamoto, M.: Some inequalities relating to the partial sum of binomial probabilities. Annals of the Institute of Statistical Mathematics 10(1), 29–35 (1959)

    Article  Google Scholar 

  10. Tempo, R., Bai, E.-W., Dabbene, F.: Probabilistic robustness analysis: explicit bounds for the minimum number of samples. Systems & Control Letters 30, 237–242 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  11. Tempo, R., Calafiore, G., Dabbene, F.: Randomized Algorithms for Analysis and Control of Uncertain Systems. Communications and Control Engineering Series. Springer, London (2005)

    MATH  Google Scholar 

  12. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)

    MATH  Google Scholar 

  13. Vidyasagar, M.: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems. Springer, London (1997)

    MATH  Google Scholar 

  14. Vidyasagar, M.: Randomized algorithms for robust controller synthesis using statistical learning theory. Automatica 37, 1515–1528 (2001)

    Article  MATH  MathSciNet  Google Scholar 

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Alamo, T., Tempo, R., Luque, A. (2010). On the Sample Complexity of Probabilistic Analysis and Design Methods. In: Willems, J.C., Hara, S., Ohta, Y., Fujioka, H. (eds) Perspectives in Mathematical System Theory, Control, and Signal Processing. Lecture Notes in Control and Information Sciences, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93918-4_4

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  • DOI: https://doi.org/10.1007/978-3-540-93918-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93917-7

  • Online ISBN: 978-3-540-93918-4

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