|
Neural networks for identification and control
Most "real world" systems relevant from a control perspective exhibit nonlinear behavior and are often
difficult / unrealistic to model using laws of physics, chemistry, etc. The neural network has shown to
be a promising technique to overcome this problem as it is useful for building good models from measured data.
|
|
The research includes the development of effective and easy-to-use training methods and techniques for finding the "optimal" network architecure.
Different input-output model structures as well as state space model structures are considered, deterministic as well as stochastic. |
|
If a neural network model is available, different approaches are possible when designing a control system, e.g., feedback
linearization, Generalized Predictive Control (GPC), or linearization of the model followed by a (time varying) linear design.
Another approach is to use a neural network as the controller as well; e.g., direct inverse control or Internal Model Control (IMC).
 Neural networks for identification and control
The NNSYSID toolbox
The NNCTRL toolkit
Textbook
Contact Ole Ravn (homepage) <or@oersted.dtu.dk>
|
