Xiang Li ; Chien Chern Cheah
Optical tweezers are capable of manipulating micro/nano objects without any physical contact, and therefore widely used in biomedical engineering and biological science. While much progress has been achieved in automated optical manipulation of micro objects, Brownian motion is commonly ignored in the stability analysis in order to simplify the control problem. However, random Brownian perturbations exist in micromanipulation problem and therefore may result in failure of optical trapping due to the escape of micro object from the trap. In addition, it is usually assumed in the development of controller that the model of trapping stiffness is known, but the model is difficult to obtain because of its spatially varying feature around the centre of laser beam and variations with laser power and dimensions of objects. In this paper, a neural-network control method is proposed for optical trapping and manipulation of micro object, in the presence of stochastic perturbations and unknown trapping stiffness. The unknown trapping stiffness and dynamic parameters of micro objects, which vary with different laser power settings and sizes of the objects, are approximated by using adaptive neural networks. The stability analysis is carried out from stochastic perspectives, by considering the effect of Brownian motion in the dynamic model. Both experimental results and simulation results are presented.
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