This work seeks to bridge the gap between feedback control and artificial intelligence. It provides design techniques for high-level neural-network feedback-control topologies that contain servo-level feedback-control loops as well as artificial intelligence decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including dynamic output feedback , reinforcement learning and optimal design , as well as a fuzzy-logic reinforcement controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.