Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for stochastic systems with traditional and emerging engineering-oriented complexities. The book begins with an overview of the relevant background, motivation, and research problems, and then:Discusses the robust stability and stabilization problems for a class of stochastic time-delay interval systems with nonlinear disturbancesInvestigates the robust stabilization and H control problems for a class of stochastic time-delay uncertain systems with Markovian switching and nonlinear disturbancesExplores the H state estimator and H output feedback controller design issues for stochastic time-delay systems with nonlinear disturbances, sensor nonlinearities, and Markovian jumping parametersAnalyzes the H performance for a general class of nonlinear stochastic systems with time delays, where the addressed systems are described by general stochastic functional differential equationsStudies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing measurement and stochastic disturbancesUses gain-scheduling techniques to tackle the probability-dependent control and filtering problems for time-varying nonlinear systems with incomplete informationEvaluates the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with multiple random communication delays and random packet lossesExamines the filtering problem for a class of nonlinear genetic regulatory networks with state-dependent stochastic disturbances and state delaysConsiders the H state estimation problem for a class of discrete-time complex networks with probabilistic missing measurements and randomly occurring coupling delaysAddresses the H synchronization control problem for a class of dynamical networks with randomly varying nonlinearitiesNonlinear Stochastic Control and Filtering with Engineering-oriented Complexities describes novel methodologies that can be applied extensively in lab simulations, field experiments, and real-world engineering practices. Thus, this text provides a valuable reference for researchers and professionals in the signal processing and control engineering communities.
Nonlinear Stochastic Control and Filtering With Engineering-Oriented Complexities
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