Neural network controller for nanopositioning of a smooth impact drive mechanism
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.
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