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Unscented Kalman filter performance for closed-loop nonlinear state estimation: a simulation case study

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Abstract

In this paper, unscented Kalman filter (UKF) is used for state estimation of nonlinear system. The main advantage of UKF is that it does not need any linearization for calculating the state transition matrix like extended Kalman filter (EKF). This study includes the combination of the nonlinear estimation and the optimal control strategy. Simulation results for a proportional integral derivative and linear quadratic regulator controlled inverted pendulum are presented and compared with the EKF under the measurement and process noise. The results show that the performance of the UKF is better than the EKF in terms of robustness, computation time and speed of convergence.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Mersin University, Çiftlikköy, 33343 , Mersin, Turkey

    Alkan Alkaya

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  1. Alkan Alkaya
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Correspondence to Alkan Alkaya.

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Alkaya, A. Unscented Kalman filter performance for closed-loop nonlinear state estimation: a simulation case study. Electr Eng 96, 299–308 (2014). https://doi.org/10.1007/s00202-014-0298-x

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