Name
Abstract | ||
|---|---|---|
In this paper, we apply iterative learning control to both linear and nonlinear fractional-order multi-agent systems to solve consensus tacking problem. Both fixed and iteration-varying communicating graphs are addressed in this paper. For linear systems, a PDα-type update law with initial state learning mechanism is introduced by virtue of the memory property of fractional-order derivative. For nonlinear systems, a Dα-type update law with forgetting factor and initial state learning is designed. Sufficient conditions for both linear and nonlinear systems are established to guarantee all agents achieving the asymptotic output consensus. Simulation examples are provided to verify the proposed schemes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jfranklin.2019.06.001 | Journal of the Franklin Institute |
Field | DocType | Volume |
Tacking,Graph,Forgetting factor,Mathematical optimization,Nonlinear system,Linear system,Control theory,Multi-agent system,Iterative learning control,Mathematics | Journal | 356 |
Issue | ISSN | Citations |
12 | 0016-0032 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dahui Luo | 1 | 0 | 0.34 |
| Jinrong Wang | 2 | 265 | 38.75 |
| Dong Shen | 3 | 155 | 17.64 |
| Michal Feckan | 4 | 92 | 10.98 |