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Passivity-based Adaptive Control of Boost Converter Based on Neural Network
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Lu WANG1, Jiuhe WANG1, Yan ZHAO2, Jianguo LI1, Yajing ZHANG  Member, CPSS1
Journal of Power Supply | 2025, 23(6) : 86 - 94
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Journal of Power Supply | 2025, 23(6): 86-94
• DC-DC Converters •
Passivity-based Adaptive Control of Boost Converter Based on Neural Network
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Lu WANG1, Jiuhe WANG1, Yan ZHAO2, Jianguo LI1, Yajing ZHANG  Member, CPSS1
Affiliations
  • 1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
  • 2.School of Information Science and Technology, Yanching Institute of Technology, Sanhe 065201, China
Published: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.86
Outline
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To improve the dynamic performance of passivity-based control (PBC) of a Boost converter with constant power load under external disturbances, a passivity-based adaptive control strategy based on neural network was proposed. Under this strategy, a passivity-based adaptive control structure with proportional and integral terms was adopted, and a single-neural network was used to optimize the injection damping, proportional coefficient and integral coefficient in real time. In addition, a power observer was used to observe the power of unknown constant power load (CPL) in real time, thus improving the dynamic and steady-state performances of the Boost converter when the CPL changes. Compared with PI+PBC and PI double-closed-loop control strategies, the passivity-based adaptive control based on neural network can make the Boost converter have a better performance. The results of computer simulations show that the proposed control strategy is feasible.

Boost converter  /  passivity-based control (PBC)  /  parameter optimization  /  neural network  /  power observer
王路, 王久和, 赵燕, 李建国, 张雅静中国电源学会会员. 基于神经网络的Boost变换器无源自适应控制. 电源学报, 2025 , 23 (6) : 86 -94 . DOI: 10.13234/j.issn.2095-2805.2025.6.86
Lu WANG, Jiuhe WANG, Yan ZHAO, Jianguo LI, Yajing ZHANG. Passivity-based Adaptive Control of Boost Converter Based on Neural Network[J]. Journal of Power Supply, 2025 , 23 (6) : 86 -94 . DOI: 10.13234/j.issn.2095-2805.2025.6.86
Funding
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  • National Natural Science Foundation of China(51777012; 52237008)
  • Project Jointly Funded by Beijing Natural Science Foundation and Education Commission(KZ201911232045)
Appendix
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Year 2025 volume 23 Issue 6
44
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.6.86
  • Receive Date：2022-12-08
  • Online Date：2026-10-26
  • Published：2025-10-30
Article Data
Affiliations
History
  • Received：2022-12-08
  • Revised：2023-02-04
  • Accepted：2023-03-07
Funding
National Natural Science Foundation of China(51777012; 52237008)
Project Jointly Funded by Beijing Natural Science Foundation and Education Commission(KZ201911232045)
Affiliations
    1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
    2.School of Information Science and Technology, Yanching Institute of Technology, Sanhe 065201, China
References
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