Neural Network Controller for Manipulation of Micro-Scale Objects
Abstract
In this paper, a novel reinforcement learning-Based neural network (RLNN) controller is presented for the manipulation and handling of micro-scale objects in a micro-electromechanical system (MEMS). in MEMS, adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. the RLNN controller consists of an action NN for compensating the unknown system dynamics, and a critic NN to tune the weights of the action NN. using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error and weight estimates are shown by using a novel weight updates. Simulation results are presented to substantiate the theoretical conclusions. © 2004 IEEE.
Recommended Citation
V. Janardhan et al., "Neural Network Controller for Manipulation of Micro-Scale Objects," IEEE International Symposium on Intelligent Control - Proceedings, pp. 55 - 60, Institute of Electrical and Electronics Engineers, Dec 2004.
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2004
