Ha. Talebi et al., Neural network based dynamic modeling of flexible-link manipulators with application to the SSRMS, J ROBOTIC S, 17(7), 2000, pp. 385-401
This paper presents an approach for dynamic modeling of flexible-link manip
ulators using artificial neural networks. A state-space representation is c
onsidered for a neural identifier. A recurrent network configuration is obt
ained by a combination of feedforward network architectures with dynamical
elements in the form of stable filters. To guarantee the boundedness of the
states, a joint PD control is introduced in the system. The method can be
considered both as an online identifier that can be used as a basis for des
igning neural network controllers as well as an offline learning scheme to
compute deflections due to Link flexibility for evaluating forward dynamics
. Unlike many other methods, the proposed approach does not assume knowledg
e of the nonlinearities of the system nor that the nonlinear system is line
ar in parameters. The performance of the proposed neural identifier is eval
uated by identifying the dynamics of different flexible-link manipulators.
To demonstrate the effectiveness of the algorithm, simulation results for a
single-link manipulator, a two-link planar manipulator, and the Space Stat
ion Remote Manipulator System (SSRMS) are presented. (C) 2000 John Wiley &
Sons, Inc.