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Energy-Aware Multicriteria Control Performance Assessment

Author

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  • Paweł D. Domański

    (Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland)

Abstract

Generally, control system design and the associated assessment of control system quality focuses on cutting-edge performance. Most of the approaches and applied indicators aim for this goal. However, the current times increasingly indicate the need to consider, at least on an equal level, the issue of the resistance of the control system and the energy that it consumes. Indicators for the assessment of the quality of control system operation should take these aspects into account. This study focuses on energy issues. It should be noted that, very often, an actuator device, such as a pump, motor, or actuator, consumes energy. In small single-loop systems, the share of this energy is usually negligible, but in large installations, it begins to reach significant values. This work proposes a multi-criteria assessment of the operation of control systems using information about the control signal. The energy factor can be considered in the form of a quadratic relationship or using the valve travel and valve stroke indicators known in other contexts. The index ratio diagram (IRD) approach is utilized as an energy assessment tool. At the same time, an analysis is carried out showing the impact of energy on other known indicators based on the control error. Finally, a methodology incorporating energy consumed by the control system is proposed.

Suggested Citation

  • Paweł D. Domański, 2024. "Energy-Aware Multicriteria Control Performance Assessment," Energies, MDPI, vol. 17(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1173-:d:1349508
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    1. Elvezio Ronchetti, 2021. "The main contributions of robust statistics to statistical science and a new challenge," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 127-135, August.
    2. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-216, April.
    3. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    4. Yongcheng Qi, 2010. "On the tail index of a heavy tailed distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(2), pages 277-298, April.
    5. Burnecki, Krzysztof & Sikora, Grzegorz, 2017. "Identification and validation of stable ARFIMA processes with application to UMTS data," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 456-466.
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