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9781118929490 1118929497 Establishes a unified framework for filtering and control problems for various discrete-time nonlinear stochastic systems with engineering-oriented complications such as parameter uncertainties, missing measurements, sensor/actuator faults and degraded outputs, which are typical phenomena resulting from the complexity in nowadays complex systems., Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges, Variance-Constrained Multi-Objective Stochastic Control and Filtering establishes a unified framework for filtering and control problems for various discrete-time nonlinear stochastic systems with engineering-oriented complications such as parameter uncertainties, missing measurements, sensor/actuator faults and degraded outputs, which are typical phenomena resulting from the complexity in nowadays complex systems. Multiple performance requirements are simultaneously considered, which include the regional stability, steady-state variance, robustness, disturbance rejection attenuation, integrity against missing measurements, reliability against sensor/actuator failures, dissipativity and energy constraint. A set of latest techniques are covered to handle the emerging mathematical/computational challenges involved. This book covers this developing area of control and filtering theories for stochastic systems with multiple objective and variance constraints typically resulting from complex environments.
9781118929490 1118929497 Establishes a unified framework for filtering and control problems for various discrete-time nonlinear stochastic systems with engineering-oriented complications such as parameter uncertainties, missing measurements, sensor/actuator faults and degraded outputs, which are typical phenomena resulting from the complexity in nowadays complex systems., Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges, Variance-Constrained Multi-Objective Stochastic Control and Filtering establishes a unified framework for filtering and control problems for various discrete-time nonlinear stochastic systems with engineering-oriented complications such as parameter uncertainties, missing measurements, sensor/actuator faults and degraded outputs, which are typical phenomena resulting from the complexity in nowadays complex systems. Multiple performance requirements are simultaneously considered, which include the regional stability, steady-state variance, robustness, disturbance rejection attenuation, integrity against missing measurements, reliability against sensor/actuator failures, dissipativity and energy constraint. A set of latest techniques are covered to handle the emerging mathematical/computational challenges involved. This book covers this developing area of control and filtering theories for stochastic systems with multiple objective and variance constraints typically resulting from complex environments.