Abstract
In this chapter, the probability-guaranteed \(H_\infty \) finite-horizon filtering problem is investigated for a class of nonlinear time-varying systems with uncertain parameters and sensor saturations. The system matrices are functions of mutually independent stochastic variables that obey uniform distributions over known finite ranges. Attention is focused on the construction of a time-varying filter such that the prescribed \(H_\infty \) performance requirement can be guaranteed with probability constraint. By using the difference linear matrix inequalities (DLMIs) approach, sufficient conditions are established to guarantee the desired performance of the designed finite-horizon filter. The time-varying filter gains can be obtained in terms of the feasible solutions to a set of DLMIs that can be recursively solved by using the semidefinite programming method. A computational algorithm is specifically developed for the addressed probability-guaranteed \(H_\infty \) finite-horizon filtering problem.
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Hu, J., Wang, Z., Gao, H. (2015). Probability-Guaranteed \(H_\infty \) Finite-Horizon Filtering with Sensor Saturations. In: Nonlinear Stochastic Systems with Network-Induced Phenomena. Springer, Cham. https://doi.org/10.1007/978-3-319-08711-5_4
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DOI: https://doi.org/10.1007/978-3-319-08711-5_4
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Online ISBN: 978-3-319-08711-5
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