Stable, controllable neural control of affine uncertain systems

This paper describes an approach to using neural networks as part of a control architecture that allows tracking performance to improve as the network assimilates the dynamic characteristics of the plant. Stability is guaranteed by using Lyapunov analysis and controllability is insured as the network learns. The results are shown for a scalar, affine plant where uncertainties exist in the functions representing the dynamics of the plant.