Name
Title | ||
|---|---|---|
Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks. |
Abstract | ||
|---|---|---|
Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00521-016-2695-8 | Neural Computing and Applications |
Keywords | Field | DocType |
Radial basis function networks, One-link and two-link robotic manipulators, Identification and adaptive control, Multi layer feed-forward neural network, Robustness | Radial basis function network,Control theory,Gradient descent,Control theory,Mean squared error,Robustness (computer science),Artificial intelligence,Adaptive control,Cluster analysis,Artificial neural network,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
30 | 1 | 1433-3058 |
Citations | PageRank | References |
2 | 0.36 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajesh Kumar | 1 | 12 | 9.32 |
| Smriti Srivastava | 2 | 137 | 19.60 |
| J. R. P. Gupta | 3 | 51 | 6.26 |