Name
Title | ||
|---|---|---|
RNN for Perturbed Manipulability Optimization of Manipulators Based on a Distributed Scheme: A Game-Theoretic Perspective |
Abstract | ||
|---|---|---|
In order to leverage the unique advantages of redundant manipulators, avoiding the singularity during motion planning and control should be considered as a fundamental issue to handle. In this article, a distributed scheme is proposed to improve the manipulability of redundant manipulators in a group. To this end, the manipulability index is incorporated into the cooperative control of multiple manipulators in a distributed network, which is used to guide manipulators to adjust to the optimal spatial position. Moreover, from the perspective of game theory, this article formulates the problem into a Nash equilibrium. Then, a neural network with anti-noise ability is constructed to seek and approximate the optimal strategy profile of the Nash equilibrium problem with time-varying parameters. Theoretical analyses show that the neural network model has the superior global convergence and noise immunity. Finally, simulation results demonstrate that the neural network is effective in real-time cooperative motion generation of multiple redundant manipulators under perturbations in distributed networks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2020.2963998 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Distributed control,game theory,manipulability optimization,neural network,redundancy resolution | Journal | 31 |
Issue | ISSN | Citations |
12 | 2162-237X | 5 |
PageRank | References | Authors |
0.40 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiazheng Zhang | 1 | 7 | 1.09 |
| Long Jin | 2 | 86 | 10.66 |
| Long Cheng | 3 | 1492 | 73.97 |