植物微生物燃料电池的自适应反步模糊控制及并网优化

樊立萍; 高振博

doi:doi:10.19912/j.0254-0096.tynxb.2024-1430

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植物微生物燃料电池的自适应反步模糊控制及并网优化

更新时间：2026-01-08

- 植物微生物燃料电池的自适应反步模糊控制及并网优化
- Vol. 46, Issue 12, Pages: 417-425(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1430
  CLC： TP29
- Published Online：07 January 2026，
  
  Published：2025
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樊立萍, 高振博. 植物微生物燃料电池的自适应反步模糊控制及并网优化[J]. 2025, 46(12): 417-425.
DOI：

樊立萍, 高振博. 植物微生物燃料电池的自适应反步模糊控制及并网优化[J]. 2025, 46(12): 417-425. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1430.

摘要

针对常规最大功率点跟踪算法（MPPT）在单室植物微生物燃料电池（SPMFC）控制中存在跟踪速度慢、局部最优等问题

结合自适应反步控制与模糊控制

提出一种混合算法

利用模糊控制器的逻辑推理能力克服非线性与耦合性对跟踪速度的影响

借助反步控制的自适应性和鲁棒性避免局部最优

并对SPMFC进行并网测试。结果表明：自适应反步-模糊控制使得SPMFC获得更大的输出功率和更快的跟踪速度

既实现对SPMFC最大功率点的无差跟踪

又能够保障并网后的电网对称

有效解决SPMFC产电功率低、MPPT出现局部最优及并网时网侧不对称等问题。

Abstract

To address the issues of slow tracking speed and local optimum of conventional maximum power point tracking （MPPT） algorithms for single chamber plant microbial fuel cells （SPMFC）

a hybrid algorithm combining adaptive backstepping control and fuzzy control is proposed. This algorithm utilizes the logical reasoning capabilities of a fuzzy controller to overcome the effects of nonlinearity and coupling on tracking speed

and leverages the adaptability and robustness of backstepping control to avoid local optimum. Grid-connected tests are conducted on the SPMFC. The results indicate that the adaptive backstepping-fuzzy control enables the SPMFC to achieve higher output power and faster tracking speed

ensuring accurate tracking of the maximum power point and maintaining grid symmetry after grid connection. This effectively solves the issues of low power generation in SPMFCs

local optimum in MPPT

and asymmetry on the grid side during grid connection.

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references

樊立萍, 童兵. 基于BOOST变换器的MFC最大功率跟踪控制[J]. 太阳能学报, 2021, 42(2): 274-280.

LI D D, ZHAO Y Q, WEI D, et al.Key issues to consider toward an efficient constructed wetland-microbial fuel cell: the idea and the reality[J]. Environmental science and pollution research, 2024, 31(8): 11559-11575.

FU Y B, CHEN J Q, CHEN Y, et al.Humic acid and iron chelation modified anode improves the electrochemical performance of marine sediment microbial fuel cell[J]. Journal of Ocean University of China, 2022, 21(2): 388-394.

樊立萍, 杨馀瑛. PVA/OPD改性膜对微生物燃料电池性能的影响[J]. 精细化工, 2023, 40(1): 169-176.

FAN L P, CHEN X M.Optimization of controller for microbial fuel cell: comparison between genetic algorithm and fuzzy logic[J]. International journal of electrochemical science, 2021, 16(11): 211123.

李建林, 梁忠豪, 李光辉, 等. 太阳能制氢关键技术研究[J]. 太阳能学报, 2022, 43(3): 2-11.

WANG Y, LI K, YANG K H, et al.Adaptive backstepping control for spacecraft rendezvous on elliptical orbits based on transformed variables model[J]. International journal of control, automation and systems, 2018, 16(1): 189-196.

高超, 姚秀萍, 刘日新, 等. 基于自适应控制的风光制储氢协调运行策略研究[J]. 太阳能学报, 2023, 44(8): 102-109.

BEŞKARDEŞ A, HAMEŞ Y, KAYA K. A comprehensive review on fuzzy logic control systems for all, hybrid, and fuel cell electric vehicles[J]. Soft computing, 2024, 28(13): 8183-8221.

KHADHRAOUI A, ZOUAOUI A, SAAD M.Barrier Lyapunov function and adaptive backstepping-based control of a quadrotor UAV[J]. Robotica, 2023, 41(10): 2941-2963.

PAMINTUAN K R S, KATIPUNAN A M C, PALAGANAS P A O, et al. An analysis of the stacking potential and efficiency of plant-microbial fuel cells growing green beans (vigna ungiculata ssp. sesquipedalis)[J]. International journal of renewable energy development, 2020, 9(3): 439-447.

NARAYANA PRASAD P, KALLA S.Plant-microbial fuel cells:a bibliometric analysis[J]. Process biochemistry, 2021, 111: 250-260.

PATEL R, DEB D.Adaptive backstepping control of single chamber microbial fuel cell[C]//2017 17th International Conference on Control, Automation and Systems (ICCAS). Jeju, Korea, 2017: 574-579.

HAI T, AKSOY M, NISHIHARA K.Optimized MPPT model for different environmental conditions to improve efficacy of a photovoltaic system[J]. Soft computing, 2024, 28(3): 2161-2179.

帅逸轩, 赵培轩, 刘慧敏, 等. 基于多功率耦合的风光互补制氢系统容量配置优化方法[J]. 太阳能学报, 2022, 43(11): 474-481.

闫桦, 窦银科, 王进, 等. 基于模糊自抗扰的PEMFC输出电压控制研究[J]. 现代电子技术, 2024, 47(14): 46-52.

DHARA S, SHRIVASTAV A K, SADHU P K.Power quality enhancement of microgrid using fuzzy logic controlled inverter and SFCL[J]. Microsystem technologies, 2024, 30(6): 687-710.

MOHAMED L, AICHA G.Implementation of fuzzy logic MPPT based on Arduino in small scale PV pumping system[J]. International journal of power electronics and drive systems (IJPEDS), 2024, 15(1): 192.

樊立萍, 陈其鹏. 基于萤火虫优化模糊P&O的微生物燃料电池最大功率点跟踪[J]. 太阳能学报, 2024, 45(4): 373-381.

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