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Non-Statistical Method for Validation the Time Characteristics of Digital Control Systems with a Cyclic Processing Algorithm

Author

Listed:
  • Vitaly Promyslov

    (V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, 117797 Moscow, Russia)

  • Kirill Semenkov

    (V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, 117797 Moscow, Russia)

Abstract

The paper discusses the problem of performance and timing parameters with respect to the validation of digital instrumentation and control systems (I&C). Statistical methods often implicitly assume that the probability distribution law of the estimated parameters is close to normal. Thus, the confidence intervals for the parameter are determined on the grounds of this assumption. However, we encountered cases when the delay distribution law in I&C is not normal. In these cases, we used the non-statistical network calculus method for time parameters estimation. The network calculus method is well elaborated for lossless digital system models with seamless processing algorithm depending only on data volume. We consider the extension of the method to the case of I&C systems with considerable changes in the data flow and content-dependent processing disciplines. The model is restricted to systems with cyclic processing algorithms and fast network connections. Network calculus describes the data flow and system parameters in terms of flow envelopes and service curves that are generally unknown in advance. In this paper, we define equations that allow the calculation of these characteristics from experimental data. The correspondence of the Network Calculus and classical statistical estimation methods is discussed. Additionally, we give an example of model application to a real I&C system.

Suggested Citation

  • Vitaly Promyslov & Kirill Semenkov, 2021. "Non-Statistical Method for Validation the Time Characteristics of Digital Control Systems with a Cyclic Processing Algorithm," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1732-:d:599394
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    References listed on IDEAS

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    1. Eduardo Camponogara & Luiz Fernando Nazari, 2015. "Models and Algorithms for Optimal Piecewise-Linear Function Approximation," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, July.
    2. Krzysztof Burnecki & Agnieszka Wylomanska & Aleksei Chechkin, 2015. "Discriminating between Light- and Heavy-Tailed Distributions with Limit Theorem," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-23, December.
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