ABSTRACT
A neural network controller is designed to effectively control a plant with drifting parameters. In response to parameter drift the parent network transitions to a specifically trained child control network exhibiting increased accuracy in the new parameter space if such accuracy is necessary and attainable. The controlling neural network itself possesses efficiently trained outputs to diagnose the drift of crucial plant parameters. Results are demonstrated for two plants, one linear, the other nonlinear.
