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This book is concerned with the variance-constrained optimized filtering problems and their potential applications for nonlinear time-varying dynamical systems. The distinguished features of this book are highlighted as follows.
(1) A unified framework is provided for handling the variance-constrained filtering problems of nonlinear time-varying dynamical systems with incomplete information.
(2) The application potentials of variance-constrained optimized filtering in networked time-varying dynamical systems are outlined. It contains some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.
It is a collection of several research results and thereby serves as a useful reference for upper undergraduate, postgraduate and engineers who are interested in studying (i) the variance-constrained filtering, (ii) recent advances affected by incomplete information and (iii) potential applications in practical engineering systems.
Table des matières
Introduction.- Recursive Filtering and Boundedness Analysis with ROQ.- Resilient Filtering with Stochastic Uncertainties and Incomplete Measurements.- Event-Triggered Resilient Filtering with Stochastic Uncertainties and SPDs.- Event-triggered Filtering with Missing Measurements.- Fault Estimation Against Randomly Occurring Deception Attacks.- Fault Estimation with Packet Dropouts and ROUs.- Fault Estimation with Randomly Occurring Faults and Sensor Saturations.- State Estimation for Complex Networks with Missing Measurements.- Quantized State Estimation for Complex Networks with Uncertain Inner Coupling.- Event-Based State Estimation for Complex Networks under UOPs.- Event-Based State Estimation for Complex Networks with Fading Observations and UST.- State Estimation for Complex Networks with Uncertain Observations and Coupling Strength.- Conclusions and Future Work.
