This book discusses how to deal with such constraints to guarantee the system’s design objectives, focusing on real-world dynamical systems such as Markovian jump systems, networked control systems, neural networks, and complex networks, which have recently excited considerable attention.
Introduction.- Network-based Control with Asynchronous Samplings and Quantizations.- Quantized Static Output Feedback Control For Discrete-Time Systems.- Sampled-Data Control for a Class of Linear Systems with Randomly Occurring Missing Data.- Reliable Event-triggered Retarded Dynamic Output Feedback H∞ Control for Networked Systems.- Reliable H∞ Event-triggered Control for Markov Jump Systems.- Fuzzy Resilient Energy-to-Peak Filter Design for Continuous-time Nonlinear Systems.- Fuzzy Generalized H2 Filtering For Nonlinear Discrete-Time Systems With Measurement Quantization.- Event-triggered Dissipative Filtering for Networked semi-Markov Jump Systems.- Network-based H∞ State Estimation for Neural Networks Using Limited Measurement.- Mixed H∞/passive Synchronization for Complex Dynamical Networks with Sampled-data Control.- Index.