Research Area:  Machine Learning

Abstract:

Redundancy resolution is a critical problem in the control of manipulators. The dual neural network, as a special type of recurrent neural networks that are inherently parallel processing models, is widely investigated in past decades to control manipulators. However, to the best of our knowledge, existing dual neural networks require a full knowledge about manipulator parameters for efficient control. We make progress along this direction in this paper by proposing a novel model-free dual neural network, which is able to address the learning and control of manipulators simultaneously in a unified framework. Different from pure learning problems, the interplay of the control part and the learning part allows us to inject an additive noise into the control channel to increase the richness of signals for the purpose of efficient learning. Due to a deliberate design, the learning error is guaranteed for convergence to zero despite the existence of additive noise for stimulation. Theoretical analysis reveals the global stability of the proposed neural network control system. Simulation results verify the effectiveness of the proposed control scheme for redundancy resolution of a PUMA 560 manipulator.

Keywords:  
Dynamic Neural Network
recurrent neural networks
redundant manipulator
simultaneous learning and control
Machine Learning
Deep Learning

Author(s) Name:  Shuai Li; Zili Shao; Yong Guan

Publisher name:  IEEE

DOI:  10.1109/TSMC.2017.2690460

Volume Information:  ( Volume: 49, Issue: 5, May 2019) Page(s): 932 - 941

Paper Link:   https://ieeexplore.ieee.org/abstract/document/7904747