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 disturbances
Investigates the robust stabilization and H control problems for a class of stochastic time-delay uncertain systems with Markovian switching and nonlinear disturbances
Explores the H state estimator and H output feedback controller design issues for stochastic time-delay systems with nonlinear disturbances, sensor nonlinearities, and Markovian jumping parameters
Analyzes 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 equations
Studies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing measurement and stochastic disturbances
Uses gain-scheduling techniques to tackle the probability-dependent control and filtering problems for time-varying nonlinear systems with incomplete information
Evaluates the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with multiple random communication delays and random packet losses
Examines the filtering problem for a class of nonlinear genetic regulatory networks with state-dependent stochastic disturbances and state delays
Considers the H state estimation problem for a class of discrete-time complex networks with probabilistic missing measurements and randomly occurring coupling delays
Addresses the H synchronization control problem for a class of dynamical networks with randomly varying nonlinearities
Nonlinear 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.
