Variable Gain Iterative Learning Control with Forgetting Factor
In order to improve the convergence speed of iterative learning control and reduce the fluctuation of the system error, a class of linear steady-state systems is considered. The convergence of the algorithm and error fluctuations are studied by introducing the variable-gain idea into the D-type iter...
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| Veröffentlicht in: | IOP conference series. Materials Science and Engineering 2018-07, Vol.394 (5), p.52082 |
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| description | In order to improve the convergence speed of iterative learning control and reduce the fluctuation of the system error, a class of linear steady-state systems is considered. The convergence of the algorithm and error fluctuations are studied by introducing the variable-gain idea into the D-type iterative learning control algorithm with variable forgetting factor. According to the related properties of the λ norm theory, the convergence of the improved iterative learning algorithm is proved. Compared with iterative learning control with forgetting factor and iterative learning control with variable gain, MATLAB simulation analysis is performed. The simulation results show that the algorithm is effective. The improved iterative learning law not only makes the iterative error smoother, but also improves the convergence speed. |
| doi_str_mv | 10.1088/1757-899X/394/5/052082 |
| format | Article |
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| source | Institute of Physics IOPscience extra; IOP Publishing Free Content; EZB-FREE-00999 freely available EZB journals; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Control algorithms Control theory Convergence Error analysis Machine learning Variable gain |
| title | Variable Gain Iterative Learning Control with Forgetting Factor |
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