Abstract
In this chapter focus on the \(\ell _2\)-\(\ell _{\infty }\) filter design problem for Markovian jump repeated scalar nonlinear systems. The main contributions of this chapter can be summarized as follows: (1) a novel nonlinear system model with a Markov process is introduced, which is described by a discrete-time state equation involving a repeated scalar nonlinearity that typically appears in recurrent neural networks and hybrid systems with finite discrete operation modes; (2) based on the mode-dependent positive definite diagonally dominant Lyapunov function approach, a sufficient condition is obtained, which guarantees that the corresponding filtering error system is stochastically stable and has a prescribed \(\ell _{2}\)-\(\ell _{\infty }\) performance; (3) a sufficient condition for existence of admissible controllers is obtained in terms of matrix equalities, and a cone complementarity linearization (CCL) procedure is employed to transform a nonconvex feasibility problem into a sequential minimization problem subject to LMIs, which can be readily solved by existing optimization techniques; and (4) full- and reduced-order filters are designed in a unique framework.
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© 2016 Springer International Publishing Switzerland
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Yao, X., Wu, L., Zheng, W.X. (2016). Filtering of Markovian Jump Repeated Scalar Nonlinear Systems. In: Filtering and Control of Stochastic Jump Hybrid Systems. Studies in Systems, Decision and Control, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-319-31915-5_6
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DOI: https://doi.org/10.1007/978-3-319-31915-5_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31914-8
Online ISBN: 978-3-319-31915-5
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