Search

In this paper, an adaptive growing-and-pruning neural network control (AGPNNC) system is developed for a linear piezoelectric ceramic motor. The AGPNNC system is comprised of a neural controller and a robust controller. The neural controller utilizes a self-constructing neural network (SCNN) to mimic an ideal computation controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. If the hidden neuron of the SCNN is insignificant, it should be removed to reduce the computation load; otherwise, if the hidden neuron of the SCNN is significant, it should be retained. Finally, the experimental results show that a perfect tracking response can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive growing-and-pruning neural network control (AGPNNC) system is developed for a linear piezoelectric ceramic motor. The AGPNNC system is comprised of a neural controller and a robust controller. The neural controller utilizes a self-constructing neural network (SCNN) to mimic an ideal computation controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. If the hidden neuron of the SCNN is insignificant, it should be removed to reduce the computation load; otherwise, if the hidden neuron of the SCNN is significant, it should be retained. Finally, the experimental results show that a perfect tracking response can be achieved. [ABSTRACT FROM AUTHOR]