A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem
When the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions. Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed. Whi...
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| Veröffentlicht in: | IEEE access 2023-01, Vol.11, p.1-1 |
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| creator | Wang, Lei Dong, Liangxin Huangfu, Ziwei Chen, Yiyang |
| description | When the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions. Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed. While the entire control table is affected by the modifiable factors selection table, all previous control parameters need to be reset. In light of above problems, this paper firstly proposes new fuzzy controller design methods, which retain strengths of traditional controller and controller with modifiable factors. It effectively overcomes the shortcomings of the two controllers mentioned above, and only need to adjust the compromise factor for different working conditions of proposed controller, which is more convenient and efficient. Secondly, the proposed fuzzy controller also adopts a four-layer neural network to optimize the control rules of compromise to improve control precision and system robustness. Finally, the excellent characteristics of proposed controller are verified through simulation research, and the simulation result proves the proposed fuzzy controller has the advantages of higher control precision and smaller transition. |
| doi_str_mv | 10.1109/ACCESS.2023.3246265 |
| format | Article |
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| subjects | Biological neural networks compromise features control precision Control systems design Controllers Design methodology Fuzzy control Fuzzy controller Fuzzy logic neural network Neural networks Neurons Optimization Parameter modification Robust control Robustness Training |
| title | A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem |
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