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Comprehensive online estimation of object signals for a control system with an adaptive approach and incomplete measurements

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Języki publikacji
EN
Abstrakty
EN
This paper presents the novel estimation algorithm that generates all signals of an object described by nonlinear ordinary differential equations based only on easy-to-implement measurements. Unmeasured signals are estimated by using an adaptive approach. For this purpose, a filtering equation with a continuously modified gain vector is used. Its value is determined by an incremental method, and the amount of correction depends on the current difference between the generated signal and its measured counterpart. In addition, the study takes into account the aging process of measurements and their random absence. The application of the proposed approach can be realized for any objects with a suitable mathematical description. A biochemically polluted river with an appropriate transformation of the notation of partial differential equations was chosen as an object. The results of numerical experiments are promising, and the process of obtaining them involves little computational necessity, so the approach is aimed at the needs of control implemented online.
Rocznik
Strony
art. no. e150111
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Faculty of Technical Engineering, State University of Applied Sciences in Jaroslaw, Poland
  • Faculty of Technical Engineering, State University of Applied Sciences in Jaroslaw, Poland
  • Faculty of Technical Engineering, State University of Applied Sciences in Jaroslaw, Poland
  • Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Poland
  • Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Poland
Bibliografia
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  • [14] P. Hawro, T.Kwater, and D. Strzęciwilk, “The monitoring system based on lookup algorithm for objects described by ordinary differential equations,” ITM Web Conf.s, vol. 21, p. 00006, 10 2018. [Online]. Available: https://www.itm-conferences.org/10.1051/itmconf/20182100006
  • [15] P. Hawro, T. Kwater, J. Bartman, and B. Kwiatkowski, “The look-up algorithm of monitoring an object described by nonlinear ordinary differential equations,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 71, no. 2, p. e144603, 2023.
  • [16] A.N. Ahmed, F.B. Othman, H.A. Afan, R.K. Ibrahim, C.M. Fai, M.S. Hossain, M. Ehteram, and A. Elshafie, “Machine learning methods for better water quality prediction,” J. Hydrol., vol. 578, no. 11, p. 124084, 2019, [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0022169419308194
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  • [21] C. Tang, Y. Yi, Z. Yang, and J. Sun, “Risk forecasting of pollution accidents based on an integrated bayesian network and water quality model for the south to north water transfer project,” Ecol. Eng., vol. 96, nno. 11, pp. 109–116, 2016. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0925857415302809
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  • [24] J.-M. Guihal, F. Auger, N. Bernard, and E. Schaeffer, “Efficient implementation of continuous-discrete extended kalman filters for state and parameter estimation of nonlinear dynamic systems,” IEEE Trans. Ind. Inform., vol. 18, no. 5, pp. 3077–3085, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9527070/
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  • [27] T. Chai and R.R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)? – arguments against avoiding rmse in the literature,” Geosci. Model Dev., vol. 7, pp. 1247–1250, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3596422d-c56c-4311-9cdc-312d4c0bb091
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