GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SP...
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/624 |
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| author | Mingjun Kuang Qingwen Hou Jindong Wang Jianping Guo Zhengjun Wei |
| author_facet | Mingjun Kuang Qingwen Hou Jindong Wang Jianping Guo Zhengjun Wei |
| author_sort | Mingjun Kuang |
| collection | DOAJ |
| description | This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms. |
| format | Article |
| id | doaj-art-e54956fb8de041d79f58cb04f76f89b6 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-e54956fb8de041d79f58cb04f76f89b62025-08-20T03:08:12ZengMDPI AGMachines2075-17022025-07-0113762410.3390/machines13070624GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal ManagementMingjun Kuang0Qingwen Hou1Jindong Wang2Jianping Guo3Zhengjun Wei4Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, South China Normal University, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, South China Normal University, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, South China Normal University, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, South China Normal University, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, South China Normal University, Guangzhou 510006, ChinaThis study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms.https://www.mdpi.com/2075-1702/13/7/624ANFIS-PIDgenetic algorithmhybrid adaptive controlsingle-photon avalanche diodesthermal regulation |
| spellingShingle | Mingjun Kuang Qingwen Hou Jindong Wang Jianping Guo Zhengjun Wei GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management Machines ANFIS-PID genetic algorithm hybrid adaptive control single-photon avalanche diodes thermal regulation |
| title | GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management |
| title_full | GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management |
| title_fullStr | GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management |
| title_full_unstemmed | GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management |
| title_short | GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management |
| title_sort | ga synthesized training framework for adaptive neuro fuzzy pid control in high precision spad thermal management |
| topic | ANFIS-PID genetic algorithm hybrid adaptive control single-photon avalanche diodes thermal regulation |
| url | https://www.mdpi.com/2075-1702/13/7/624 |
| work_keys_str_mv | AT mingjunkuang gasynthesizedtrainingframeworkforadaptiveneurofuzzypidcontrolinhighprecisionspadthermalmanagement AT qingwenhou gasynthesizedtrainingframeworkforadaptiveneurofuzzypidcontrolinhighprecisionspadthermalmanagement AT jindongwang gasynthesizedtrainingframeworkforadaptiveneurofuzzypidcontrolinhighprecisionspadthermalmanagement AT jianpingguo gasynthesizedtrainingframeworkforadaptiveneurofuzzypidcontrolinhighprecisionspadthermalmanagement AT zhengjunwei gasynthesizedtrainingframeworkforadaptiveneurofuzzypidcontrolinhighprecisionspadthermalmanagement |