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• W2034745144 abstract "A moving-horizon state estimation problem is addressed for a class of nonlinear discrete-time systems with bounded noises acting on the system and measurement equations. As the statistics of such disturbances and of the initial state are assumed to be unknown, we use a generalized least-squares approach that consists in minimizing a quadratic estimation cost function defined on a recent batch of inputs and outputs according to a sliding-window strategy. For the resulting estimator, the existence of bounding sequences on the estimation error is proved. In the absence of noises, exponential convergence to zero is obtained. Moreover, suboptimal solutions are sought for which a certain error is admitted with respect to the optimal cost value. The approximate solution can be determined either on-line by directly minimizing the cost function or off-line by using a nonlinear parameterized function. Simulation results are presented to show the effectiveness of the proposed approach in comparison with the extended Kalman filter." @default.
• W2034745144 created "2016-06-24" @default.
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• W2034745144 date "2008-07-01" @default.
• W2034745144 modified "2023-10-10" @default.
• W2034745144 title "Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes" @default.
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• W2034745144 doi "https://doi.org/10.1016/j.automatica.2007.11.020" @default.
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