Neural network identification and control of unstable systems using supervisory control while learning

Abstract

Focuses on the training scheme for the neural networks to learn in the regions of unstable equilibrium states and the identification and the control using these networks. These can be achieved by introducing a supervisory controller during the learning period of the neural networks. The supervisory controller is designed based on Lyapunov theory and it guarantees the boundedness of the system states within the region of interest. Therefore the neural networks can be trained to approximate sufficiently accurately with uniformly distributed training samples by properly choosing the desired states covering the region of interest. After the networks are successfully trained to identify the system, the controller is designed to cancel out the nonlinearity of the system.<>