Neurocontrol II: High precision control achieved using approximate inverse dynamics models
Cs. Szepesvári and A. Lõrincz
Neural Network World
6,
897--920
(1996)
Abstract
It is common that artificial neural networks (ANNs) are used for
approximating the inverse dynamics of a plant. In the
accompanying paper a self-organising ANN model for associative
identification of the inverse dynamics was introduced.
Here we propose the
use of approximate inverse dynamic models
for both Static and Dynamic State (SDS)
feedback control. This compound controller is capable of
high-precision control even when the inverse dynamics is just
qualitatively modeled or the plant's dynamics is perturbed.
Properties of the SDS Feedback Controller in learning the inverse
dynamics
as well as comparisons with other methods are discussed.
An example is presented when a chaotic plant, a bioreactor,
is controlled using the SDS Controller. We found that the SDS
Controller can compensate model mismatches that otherwise
would lead to an untolerably large error
if a traditional controller were used.
