Comparison of Adaptive Kalman Filter Methods in State Estimation of a Nonlinear System Using Asynchronous Measurements

This paper presents the state estimation problem for nonlinear industrial systems using asynchronous measurements to simulate the circumstances of real case studies. The well-known conventional Kalman filters give the optimal solution but require synchronous measurements, an accurate system model and exact stochastical noise characteristics. Thus, the Kalman filter with incomplete information and asynchronous sensors measurements may be degraded or even diverged. In order to reduce the effect of noise variance uncertainty, adaptive fading extended Kalman filter and adaptive unscented Kalman filter are proposed to overcome this drawback. On the other hand, received data to estimation nodes from multi-sensors have different communication delays and various sampling rates. In this paper, conventional Kalman filter has been modified in a way to be workable for state estimation in plants with different communication delays in their sensors. Also decentralized multi sensor fusion has been used to estimate states in presence of multi-rate sensors. The feasibility and effectiveness of the presented methods are demonstrated through simulation studies on a continuous stirred tank reactor (CSTR) benchmark problem.

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