Abstract
A novel reinforcement learning-based neural network (RLNN) controller is presented for the manipulation and handling of micro-scale objects in a microelectromechanical 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 unkoown system dynamics, and a critic NN to tune the weights of the action NN. Using the Lyapunov approach, the uniformly ultimate houndedness (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.
Recommended Citation
V. Janardhan et al., "Neural Network Controller for Manipulation of Micro-Scale Objects," Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/ISIC.2004.1387658
Meeting Name
2004 IEEE International Symposium on Intelligent Control, 2004
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2004
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
