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.
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 http://dx.doi.org/10.1109/ISIC.2004.1387658
2004 IEEE International Symposium on Intelligent Control, 2004
Electrical and Computer Engineering
Article - Conference proceedings
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