Readings Newsletter
Become a Readings Member to make your shopping experience even easier.
Sign in or sign up for free!
You’re not far away from qualifying for FREE standard shipping within Australia
You’ve qualified for FREE standard shipping within Australia
The cart is loading…
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.
$9.00 standard shipping within Australia
FREE standard shipping within Australia for orders over $100.00
Express & International shipping calculated at checkout
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.