Proposing Optimal ARMA Based Model for Measurement Compensation in the State Estimation

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.

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“…One will obtain the following equation by incorporating the value-compensated vector in Equation (5).…”

Section: Improved Compensation Using Proposed Armax Modelmentioning

confidence: 99%

“…In [4], machine learning techniques are employed for prediction purpose. In literature the Kalman filter is one of the best estimation tools among them [5]. Kalman filter is a least square error method for the estimation of a signal, or a parameter vector distorted in transmission through a channel and observed in noise.…”

Section: Introductionmentioning

confidence: 99%