Selection of Key Factors and Parameters in Assessment Algorithms

  • Mohieddine Jelali
Part of the Advances in Industrial Control book series (AIC)


Performance assessment algorithms contain many options and parameters that must be specified by the user. These factors substantially affect the accuracy and acceptability of the results of assessment exercises. A fundamental basis for performance assessment is to record and carefully inspect suitable closed-loop data. Pre-processing operations, which are suggested and those which should be strictly avoided, are given in this chapter. The first decision in control performance assessment is the choice of a (time-series) model structure for describing the net dynamic response associated with the control error. There are different possible structures and different possible identification techniques. The most widely used of them are briefly described. Particularly for MV and GMV benchmarking, it is decisive to properly select or estimate the parameters’ time delay and model orders. This topic is discussed, and some of the basic models and identification techniques concerning assessment accuracy and computational load are compared, to provide suggestions of the best suited approaches to be applied in practice.


Model Order ARMA Model Subspace Method Subspace Identification Dither Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Mohieddine Jelali
    • 1
  1. 1.Faculty of Plant, Energy and Machine SystemsCologne University of Applied SciencesKoelnGermany

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