A systematic classification of neural-network-based control

Successful industrial applications and favorable comparisons with conventional alternatives have motivated the development of a large number of schemes for neural-network-based control. Each scheme is usually composed of several independent functional features, which makes it difficult to identify precisely what is new in the scheme. Help from available overviews is therefore often inadequate, since they usually discuss only the most important overall schemes. This work breaks the available schemes down to their essential functional features and organizes the latter into a multi-level classification. The classification reveals that similar schemes often get placed in different categories, fundamentally different features often get lumped into a single category, and proposed new schemes are often merely permutations and combinations of the well-established fundamental features. The classification has two main sections: neural network only as an aid; and neural network as controller.