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High-level Feedback Control With Neural Networks
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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.
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