电源学报

基于神经网络的Boost变换器无源自适应控制

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王路 ¹ , 王久和 ¹ , 赵燕 ² , 李建国 ¹ , 张雅静中国电源学会会员 ¹

电源学报 | DC-DC变换器 2025,23(6): 86-94

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电源学报 |DC-DC变换器 2025 , 23 (6) : 86 -94

基于神经网络的Boost变换器无源自适应控制

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王路¹, 王久和¹, 赵燕², 李建国¹, 张雅静中国电源学会会员¹

作者信息

^1.北京信息科技大学自动化学院，北京　100192

^2.燕京理工学院信息科学与技术学院，三河　065201

通讯作者:

王久和（1960—　），男，通信作者，博士，教授。研究方向：电力变换器非线性控制，电能质量控制，微电网。E-mail：wjhyhrwm@163.com。

作者简介:

王路（1998—　），女，硕士研究生。研究方向：电力变换器控制器的多目标优化。E-mail：18800178908@163.com。

赵燕（1982—　），女，硕士，副教授。研究方向：智能控制及优化设计。E-mail：zhaoyan137@163.com。

李建国（1975—　），男，博士，副教授。研究方向：柔性交直流输配电及控制技术。Email：lijianguo@bistu.edu.cn。

张雅静（1982—　），女，中国电源学会会员，博士，讲师。研究方向：柔性交直流输配电及控制技术。Email：zhangyajing@bistu.edu.cn。

Passivity-based Adaptive Control of Boost Converter Based on Neural Network

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

出版时间: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.86

文章导航

摘要

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为了提升带恒功率负载情况下直流Boost变换器无源控制PBC（passivity-based control）受外部扰动时的动态性能，提出了1种基于神经网络的无源自适应控制策略。该策略采用含比例、积分项的无源自适应控制结构，利用单神经元网络对注入阻尼、比例系数、积分系数进行实时优化。此外，采用功率观测器对未知恒功率负载CPL（constant power load）的功率进行实时观测，可提高CPL变化时Boost变换器的动态和稳态性能。与PI+PBC、PI双闭环控制策略相比，基于神经网络的无源自适应控制可使Boost变换器具有更好的性能。仿真结果表明，所提控制策略具有可行性和有效性。

关键词

Boost变换器 / 无源控制 / 参数优化 / 神经网络 / 功率观测器

Abstract

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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.

Key words

Boost converter / passivity-based control (PBC) / parameter optimization / neural network / power observer

引用本文

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

基金

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国家自然科学基金资助项目(51777012; 52237008)
北京市自然科学基金-教委联合资助项目(KZ201911232045)

参考文献

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参考文献引证文献

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2025年第23卷第6期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.6.86

接收时间：2022-12-08
首发时间：2026-10-26
出版时间：2025-10-30

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出版历史

收稿日期：2022-12-08
修回日期：2023-02-04
录用日期：2023-03-07

基金

National Natural Science Foundation of China(51777012; 52237008)

国家自然科学基金资助项目(51777012; 52237008)

Project Jointly Funded by Beijing Natural Science Foundation and Education Commission(KZ201911232045)

北京市自然科学基金-教委联合资助项目(KZ201911232045)

作者信息

^1.北京信息科技大学自动化学院，北京　100192

^2.燕京理工学院信息科学与技术学院，三河　065201

通讯作者:

王久和（1960—　），男，通信作者，博士，教授。研究方向：电力变换器非线性控制，电能质量控制，微电网。E-mail：wjhyhrwm@163.com。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2025.6.86

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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