PMID- 16856658
OWN - NLM
STAT- MEDLINE
DCOM- 20060926
LR  - 20240215
IS  - 1045-9227 (Print)
IS  - 1045-9227 (Linking)
VI  - 17
IP  - 4
DP  - 2006 Jul
TI  - Stable neurovisual servoing for robot manipulators.
PG  - 953-965
LID - 10.1109/TNN.2006.875993 [doi]
AB  - In this paper, we propose a stable neurovisual servoing algorithm for set-point 
      control of planar robot manipulators in a fixed-camera configuration an show that 
      all the closed-loop signals are uniformly ultimately bounded (UUB) and converge 
      exponentially to a small compact set. We assume that the gravity term and 
      Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with 
      online real-time learning are proposed for compensating both gravitational forces 
      and errors in the robot Jacobian matrix. The learning rule for updating the 
      neural network weights, similar to a back propagation algorithm, is obtained from 
      a Lyapunov stability analysis. Experimental results on a two degrees of freedom 
      manipulator are presented to evaluate the proposed controller.
FAU - Loreto, G
AU  - Loreto G
FAU - Garrido, R
AU  - Garrido R
LA  - eng
PT  - Journal Article
PL  - United States
TA  - IEEE Trans Neural Netw
JT  - IEEE transactions on neural networks
JID - 101211035
SB  - IM
MH  - Algorithms
MH  - Learning
MH  - *Neural Networks, Computer
MH  - Photic Stimulation/*methods
MH  - Robotics/*methods
EDAT- 2006/07/22 09:00
MHDA- 2006/09/27 09:00
CRDT- 2006/07/22 09:00
PHST- 2006/07/22 09:00 [pubmed]
PHST- 2006/09/27 09:00 [medline]
PHST- 2006/07/22 09:00 [entrez]
AID - 10.1109/TNN.2006.875993 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw. 2006 Jul;17(4):953-965. doi: 10.1109/TNN.2006.875993.