Self-adaptive neural architectures for control applications

The potential use of the modeling capacity of neural networks for control applications is examined. A neuromorphic controller, called the self-adaptive neural controller (SANC), is designed by utilizing the neural modeling capacity. The results of this approach reveal at least two expected benefits: learning from example and dynamical adaptation. With the learning from example ability, SANC is essentially application-independent, even if the plant considered is too complex or too uncertain to be modeled by precise mathematical expressions. With the dynamical adaptation feature, SANC is shown to be robust, adaptive. and capable of learning, even if the environment varies too much to be controlled by traditional controllers