Name
Abstract | ||
|---|---|---|
This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760023_163 | ISNN (2) |
Keywords | Field | DocType |
normal pd control,guarantee stability,overhead crane control,neural compensation,radial basis function neural,new neural control,crane dynamic,neural compensator,overhead crane system,overhead crane,pd control,steady state,real time | Neural control,Lyapunov function,Radial basis function,Overhead crane,Computer science,Control theory,Gantry crane,Robust control,Artificial neural network,Tracking error | Conference |
Volume | ISSN | ISBN |
3972 | 0302-9743 | 3-540-34437-3 |
Citations | PageRank | References |
2 | 0.47 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rigoberto Toxqui Toxqui | 1 | 3 | 0.87 |
| Wen Yu | 2 | 283 | 22.70 |
| Xiaoou Li | 3 | 550 | 61.95 |