| 引用本文: | 杨旭红,陈 阳,贾 巍,等.基于RBF神经网络的电压外环滑模控制的Vienna整流器[J].电力系统保护与控制,2022,50(18):103-115. |
| YANG Xuhong,CHEN Yang,JIA Wei,et al.Vienna rectifier with voltage outer loop sliding mode control based on an RBF neural network[J].Power System Protection and Control,2022,50(18):103-115 |
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| 摘要: |
| 以Vienna整流器为研究对象，针对其传统电压外环滑模变结构控制不变性和对系统参数扰动敏感的问题，分析了以逼近率为基础的滑模变结构控制算法，提出了一种基于RBF神经网络的自适应电压外环滑模控制算法。该控制算法通过将RBF神经网络与滑模控制算法有效结合，同时将中点电位平衡控制加入到RBF神经网络自适应电压外环滑模控制算法的设计中，使用RBF神经网络对电压外环非线性系统进行自适应逼近，能够有效降低切换增益，削弱抖振，增强系统的抗干扰能力。最后，通过仿真分析与实验测试验证所提控制算法的有效性。将所提出的控制算法与传统滑模控制算法、PI控制算法进行比较，结果表明采用这种电压外环控制算法能够对直流输出电压目标值进行快速跟踪，平衡中点电位，改善了系统的动静态性能，提升了其抗干扰能力。 |
| 关键词: Vienna整流器 电压外环 滑模控制 趋近率 RBF神经网络 |
| DOI：DOI: 10.19783/j.cnki.pspc.211361 |
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| 基金项目:国家自然科学基金项目资助(51777120)；上海市2021年度“科技创新行动计划”项目资助(21DZ1207502) |
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| Vienna rectifier with voltage outer loop sliding mode control based on an RBF neural network |
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YANG Xuhong1, CHEN Yang1, JIA Wei2, FANG Jianfeng1, LUO Xin1, GAO Zixuan1
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(1. School of Automation Engineering, Shanghai Electric Power University, Shanghai 200090, China;
2. Shanghai Solar Energy Engineering Technology Research Center, Shanghai 200241, China)
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| Abstract: |
| A Vienna rectifier is used as the research object, and an adaptive voltage outer loop sliding mode control algorithm based on the approximation rate is analyzed for its traditional voltage outer loop sliding mode variable structure control invariance and sensitivity to system parameter perturbation. By effectively combining the RBF neural network with the sliding mode control algorithm, the algorithm also adds the midpoint potential balance control to the design of the RBF neural network adaptive voltage outer-loop sliding mode control algorithm. It uses the RBF neural network for adaptive approximation of the voltage outer-loop nonlinear system. This can effectively reduce the switching gain, weaken the jitter and enhance the anti-interference capability of the system. Lastly, simulation analysis and experimental tests are conducted to verify the effectiveness of the proposed control algorithm. The algorithm is compared with the traditional sliding mode control algorithm and the PI control algorithm, and the results show that the use of this voltage external loop control algorithm can provide fast tracking of the target value of the DC output voltage and balanced midpoint potential. This improves the dynamic and static performance of the system and enhances its anti-interference capability.
This work is supported by the National Natural Science Foundation of China (No. 51777120). |
| Key words: Vienna rectifier voltage outer ring sliding mode variable structure control near rate RBF neural network |
