In this paper, neural network theory is used to improve the positioning accuracy of smooth impact drive mechanisms (SIDMs), by designing a displacement controller that consists of a neural network identification (NNI) and a neural network controller (NNC). The dynamics of the SIDM are described by the NNI, which consists of an input layer, hidden layer, and output layer. The parameters of the NNI are adjusted using back propagation. The NNC is designed as a proportional-derivative (PD) controller, which is used to accurately control the displacement of the SIDM. The PD parameters are adjusted with an adaptive adjustment algorithm. A prototype of the SIDM was fabricated and an experimental control system was built that consists of a laser displacement sensor, power amplifier, data acquisition board, and SIDM prototype. The experimental results show that nanoscale positioning accuracy can be obtained. The control system can maintain steady operation, even if the output load mass is changed.
Smooth impact drive mechanism, nanopositioning accuracy, neural network controller, experiment system
LU, XIAOHUI; CHEN, DONG; CHENG, TINGHAI; and LI, ZHE
"Neural network controller for nanopositioning of a smooth impact drive mechanism,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
1, Article 48.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss1/48