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Testing Fault Robustness of Model Predictive Control Algorithms

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Part of the Lecture Notes in Computer Science book series (LNSC,volume 6150)

Abstract

The paper deals with the problem of evaluating fault robustness of the software implemented Dynamic Matrix Control (DMC) Model Predictive Control (MPC) algorithms. Numerical and explicit implementations of the DMC algorithms are considered. It is shown that faults affecting the algorithms can provoke undesirable behaviour or even destabilize the process. Dependability was evaluated experimentally using two different software implemented fault injection approaches, the old one (FITS) and a new one (InBochs). FITS was not sufficient in case of the numerical DMC implementation. InBochs is based on the system emulator and delivers the same level of functionality as FITS while having capability to extend fault models.

Keywords

  • dependability evaluation
  • fault injection
  • process control
  • model predictive control

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  • DOI: 10.1007/978-3-642-13556-9_7
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Gawkowski, P., Grochowski, K., Ławryńczuk, M., Marusak, P., Sosnowski, J., Tatjewski, P. (2010). Testing Fault Robustness of Model Predictive Control Algorithms. In: Giese, H. (eds) Architecting Critical Systems. ISARCS 2010. Lecture Notes in Computer Science, vol 6150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13556-9_7

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  • DOI: https://doi.org/10.1007/978-3-642-13556-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13555-2

  • Online ISBN: 978-3-642-13556-9

  • eBook Packages: Computer ScienceComputer Science (R0)