This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. However, this advance in dynamic systems also leads to unavoidable constraints. In particular, such elements in control systems involve uncertainties, transmission delays, external noise, sensor faults and failures, data packet dropouts, sampling and quantization errors, and switching phenomena, which have serious effects to the system stability and performance. This book discusses how to deal with such constraints to guarantee the systems 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. It also provides a number of practical examples to show the applicability of the presented methods and techniques.