Includes bibliographical references and index.

The authors examine optimal intelligent controller design using the Q-function for linear systems and artificial neural networks for nonlinear systems. They present the learning controller design in discrete time for networked control systems, outlining modern control techniques. They first describe the background on networked control systems, networked imperfections, and dynamical systems, stability theory, and stochastic discrete-time optimal adaptive controllers for linear and nonlinear systems. Subsequent chapters discuss the foundation of traditional Q-learning-based optimal adaptive controllers for finite and infinite horizons; quantization effects for linear and nonlinear networked control systems; a two-player zero-sum game-theoretic formulation for linear systems in input-output form enclosed by a communication network; the stochastic optimal control of nonlinear networked control systems by using neurodynamic programming; stochastic optimal design for a nonlinear two-player zero-sum game under communication constraints; distributed joint optimal network scheduling and control design for wireless networked control systems; event-sampled distributed networked control systems; and the effect of network protocols on controller design. Appendices contain analytical proofs for the controllers and MATLAB code for building intelligent controllers.