Abstract
This chapter presents two more nonlinear state estimation methods: the unscented Kalman filter (UKF) and the state-dependent Riccati equation (SDRE) observer. Though the UKF filter and SDRE observer are based on different philosophies, both of them are derivative-free, nonlinear, state estimators. Their formulations and applications to nonlinear systems are described in this chapter with illustrative application examples. Towards the end of this chapter, consideration is to be given to the robustness of state estimators in general. Uncertainty is always a crucial issue in control systems design and operations, and so is with the state estimation. After a very brief description of system dynamics perturbation and signal disturbances, the scenario of observation data missing occurring in the state estimation will be discussed. The method of linear prediction (LP) is particularly introduced to alleviate the adverse effect of data missing in the state estimation, together with a numerical example.
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Chandra, K.P.B., Gu, DW. (2019). More Estimation Methods and Beyond. In: Nonlinear Filtering. Springer, Cham. https://doi.org/10.1007/978-3-030-01797-2_7
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DOI: https://doi.org/10.1007/978-3-030-01797-2_7
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