An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

Mohseni, Adel; Doroudi, Aref; Karrari, Mehdi

doi:10.30503/eecs.2024.469359.1058

An Improved Unscented Kalman Filter Algorithm for Dynamic Systems Parameters Estimation

Document Type : Original Article

Authors

Adel Mohseni ¹

Aref Doroudi ¹

Mehdi Karrari ²

¹ Electrical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran

² Electrical Engineering Department, Amirkabir university of Technology (Tehran Polytechnic), Tehran, Iran

10.30503/eecs.2024.469359.1058

Abstract

The high capabilities of unscented Kalman filter (UKF) for estimating the state variables of a dynamic system have led to its use for parameter estimation as well. To use the UKF to estimate the unknown parameters of a dynamic system, the parameters generally be assumed in the form of virtual state variables. This paper first shows that this assumption causes some serious problems. Then, trying to solve the problem, a modified UKF algorithm will be presented. In the proposed version of the UKF algorithm, unlike the traditional one, the whole of the measurement signal samples is used as input in each stage of the estimation process. Eventually, using the proposed algorithm, the parameters of a turbine-governor system as a typical dynamic system are estimated and the efficacy of the method is investigated. The results depict that the proposed method overcomes the shortcomings of the conventional method and shows high efficiency and better performance.

Keywords

Unscented Kalman Filter

Parameter Estimation

Turbine-Governor

Subjects