Intelligent systems are a hallmark of modern feedback control systems‎.‎ But as these systems mature‎,‎ we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities‎,‎ disturbances‎,‎ unforeseen dynamics‎,‎ and unstructured uncertainties‎.‎ Artificial neural networks offer a combination of adaptability‎,‎ parallel processing‎,‎ and learning capabilities that outperform other intelligent control methods in more complex systems‎.‎ Borrowing from Biology Examining neurocontroller design in discrete‎-time for the first time‎,‎ Neural Network Control of Nonlinear Discrete‎-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems‎.‎ At every step‎,‎ the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts‎.‎ Progressive Development After an introduction to neural networks‎,‎ dynamical systems‎,‎ control of nonlinear systems‎,‎ and feedback linearization‎,‎ the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback‎,‎ system identification‎,‎ model reference adaptive control‎,‎ and novel optimal control using the Hamilton‎-Jacobi‎-Bellman formulation‎.‎ The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware‎.‎ Neural Network Control of Nonlinear Discrete‎-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations‎,‎ stability analysis‎,‎ and computer simulations‎.‎