Neural Network Based Dynamic Modeling of Flexible-Link Manipulators with Application to the SSRMS

摘要

This paper presents an approach for dynamic modeling of flexible-link manipulators using artificial neural networks. A state-space representation is considered for a neural identifier. A recurrent network configuration is obtained by a combination of feed forward network architectures with dynamical elements in the form of stable filters. To guarantee the boundednes 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 designing neural network controllers as well as an offline learning scheme to compute deflections due to link flexibility for evaluating forward dynamics. Unlink many other methods, the proposed approach does not assume knowledge of the nonlinearities of the system nor that the nonlinear system is linear in parameters. The performance of he proposed neural identifier is evaluated by identifying the dynamics of different flexible-link manipulators. To demonstrate the effectiveness of the algorithm, simulation results for a single-link manipulator, a tow-link planar manipulator, and the Space station Remote Manipulator System (SSRMS) are presented.