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

Author

Shahini, Mohsen ; Melek, William W. ; Yeow, John T W

Author_Institution

Waterloo Univ., Waterloo

fYear

2007

fDate

12-17 Aug. 2007

Firstpage

3035

Lastpage

3040

Abstract

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.

Keywords

Lyapunov methods; PD control; compensation; force control; intelligent robots; learning systems; micromanipulators; neurocontrollers; nonlinear control systems; Lyapunov function; PD controller; dynamic neural network; gravitational force; learning controller; microsized object micromanipulation; nonlinear scaling forces compensation; pushing control; weight-updating rules; Artificial neural networks; Automatic control; Electronic mail; Gravity; Insects; Intelligent robots; Mechanical engineering; Neural networks; Nonlinear dynamical systems; PD control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Conference_Location

Orlando, FL

ISSN

1098-7576

Print_ISBN

978-1-4244-1379-9

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2007.4371444

Filename

4371444