Name
Abstract | ||
|---|---|---|
The convergence rate of the traditional iterative learning control algorithm is slow, and the application range is narrow. This paper mainly focuses on the optimization of iterative learning control algorithm. It improves the traditional iterative learning control algorithm, and improves the iterative learning control algorithm through particle swarm adaptive algorithm. An adaptive optimization iterative learning control algorithm with particle swarm is proposed. Not only can the convergence speed of the algorithm be improved, but also the uncertainty of the model in the algorithm is solved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11227-018-2566-4 | The Journal of Supercomputing |
Keywords | DocType | Volume |
Particle swarm, Iterative learning control, Optimization, Constraints, Convergence | Journal | 76 |
Issue | ISSN | Citations |
5 | 1573-0484 | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qun Gu | 1 | 0 | 0.34 |
| Xiaohong Hao | 2 | 70 | 9.23 |