Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

Syed Abuzar Bacha, Gulzar Ahmad, Ghulam Hafeez, Fahad R. Albogamy and Sadia Murawwat
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Syed Abuzar Bacha: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Gulzar Ahmad: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Ghulam Hafeez: Department of Electrical and Computer Engineering, Islamabad Campus, COMSATS University Islamabad, Islamabad 44000, Pakistan
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 26571, Saudi Arabia
Sadia Murawwat: Department of Electrical Engineering, Lahore College for Women University, Lahore 54000, Pakistan

Energies, 2021, vol. 14, issue 22, 1-17

Abstract: Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this challenging issue, two techniques such as the open-loop Kalman filter and the compensating closed-loop Kalman filter have emerged. The closed-loop technique compensates for the missing data using the autoregressive model. However, the autoregressive model used only past measurements for data loss compensation. Considering only one parameter, i.e., the past measurements, is insufficient and leads to inaccurate state estimation. Thus, in this work, autoregressive moving average with exogenous inputs model considers three parameters, i.e., the past measurements, the input signal, and the sensor noise, simultaneously to compensate data loss in state estimation. To endorse the effectiveness and applicability of the proposed model, a standard mass-spring-damper is employed in the case study. Simulation results show that the proposed model outperforms the existing autoregressive models in terms of performance parameters.

Keywords: autoregressive moving average with exogenous input model; Kalman filter; linear prediction theory; loss of observation; open-loop estimation; closed-loop estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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