A Neural Network-based Learning Controller for Micro-sized Object Micromanipulation

In this paper, automated micro-sized objects manipulation is investigated. The novelty of the proposed method lies on the compensation of all the nonlinear scaling forces which are dominant over gravitational force. A dynamic neural network has been added to a PD conventional controller for automated micromanipulation control. Weight-updating rules have been obtained in such a way that the system is uniformly ultimately bounded (UUB) in the sense of Lyapunov. Simulation results for controlled pushing of a micro-object have been illustrated and the efficiency of the method has been shown by comparing its result with that of a linear controller.