Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID
In the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squ...
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MDPI AG
2024-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/1/5 |
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| author | Yan Li Miao Qian Daojing Dai Weitao Wu Le Liu Haonan Zhou Zhong Xiang |
| author_facet | Yan Li Miao Qian Daojing Dai Weitao Wu Le Liu Haonan Zhou Zhong Xiang |
| author_sort | Yan Li |
| collection | DOAJ |
| description | In the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squares online identification method is integrated to adapt to varying operating conditions. The simulation results demonstrate that in step response the WOA-improved single-neuron PID significantly mitigates the overshoot, with a mere 0.31% overshoot observed, marking a reduction of 98.27% compared to the traditional PID control. The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). The WOA-improved single-neuron PID (1.01 s) exhibited a faster return to a stable state compared to the traditional PID (2.46 s) and fuzzy PID (1.28 s). Finally, the effectiveness of the algorithm is validated through practical application. The results demonstrate that, compared to traditional PID and single-neuron PID algorithms, the WOA-improved single-neuron PID algorithm achieves an average flow stability of 9.9848 with a standard error of 0.0914394. It exhibits superior performance, including faster rise and settling times, and higher stability. |
| format | Article |
| id | doaj-art-773b0f003c054d03a0f42678303cbfb6 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-773b0f003c054d03a0f42678303cbfb62025-01-24T13:15:08ZengMDPI AGActuators2076-08252024-12-01141510.3390/act14010005Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PIDYan Li0Miao Qian1Daojing Dai2Weitao Wu3Le Liu4Haonan Zhou5Zhong Xiang6School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaZhejiang Xindebao Machinery Co., Ltd., Wenzhou 325000, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaZhejiang Yinlun New Energy Thermal Management System Co., Ltd., Taizhou 317200, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaIn the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squares online identification method is integrated to adapt to varying operating conditions. The simulation results demonstrate that in step response the WOA-improved single-neuron PID significantly mitigates the overshoot, with a mere 0.31% overshoot observed, marking a reduction of 98.27% compared to the traditional PID control. The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). The WOA-improved single-neuron PID (1.01 s) exhibited a faster return to a stable state compared to the traditional PID (2.46 s) and fuzzy PID (1.28 s). Finally, the effectiveness of the algorithm is validated through practical application. The results demonstrate that, compared to traditional PID and single-neuron PID algorithms, the WOA-improved single-neuron PID algorithm achieves an average flow stability of 9.9848 with a standard error of 0.0914394. It exhibits superior performance, including faster rise and settling times, and higher stability.https://www.mdpi.com/2076-0825/14/1/5flow boiling experimental systemWOA-improved single-neuron PIDRLSflow control |
| spellingShingle | Yan Li Miao Qian Daojing Dai Weitao Wu Le Liu Haonan Zhou Zhong Xiang Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID Actuators flow boiling experimental system WOA-improved single-neuron PID RLS flow control |
| title | Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID |
| title_full | Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID |
| title_fullStr | Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID |
| title_full_unstemmed | Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID |
| title_short | Flow Control of Flow Boiling Experimental System by Whale Optimization Algorithm (WOA) Improved Single Neuron PID |
| title_sort | flow control of flow boiling experimental system by whale optimization algorithm woa improved single neuron pid |
| topic | flow boiling experimental system WOA-improved single-neuron PID RLS flow control |
| url | https://www.mdpi.com/2076-0825/14/1/5 |
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