基于卡尔曼滤波-准谐振扩张状态观测器的MMC无模型预测控制策略

梁 备; 马文忠; 王玉生; 孟令彤; 宋曙光; 郑绍通

引用本文:	梁备,马文忠,王玉生,等.基于卡尔曼滤波-准谐振扩张状态观测器的MMC无模型预测控制策略[J].电力系统保护与控制,2026,54(06):45-57.
	LIANG Bei,MA Wenzhong,WANG Yusheng,et al.Model-free predictive control strategy of MMC based on a Kalman filtering-quasi-resonant extended state observer[J].Power System Protection and Control,2026,54(06):45-57

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基于卡尔曼滤波-准谐振扩张状态观测器的MMC无模型预测控制策略
梁备¹,马文忠¹,王玉生²,孟令彤³,宋曙光¹郑绍通¹
1.中国石油大学(华东)新能源学院，山东青岛 266580；2.中国石油天然气股份有限公司规划总院，北京 100083； 3.中国石油塔里木油田分公司，新疆库尔勒 841000

摘要:

传统有限集模型预测控制易于实现多目标控制，被广泛应用于模块化多电平换流器(modular multilevel converter, MMC)等复杂的非线性系统，但在参数失配和传感器噪声工况下性能恶化。基于此，提出一种基于卡尔曼滤波-准谐振扩张状态观测器(Kalman filtering-quasi-resonant extended state observer, KF-QRESO)的MMC无模型预测控制策略，以提高系统在参数失配和采样干扰下的鲁棒性。首先，分析参数失配下的MMC离散数学模型，构建KF-QRESO复合观测器，其中KF用于过滤采样噪声，QRESO准确估计周期交流状态量并补偿至KF状态方程中。然后，复合观测器能够降低控制系统对参数的依赖，实现对系统状态量的精确估计。同时，研究观测器对周期信号的跟踪能力及其稳定性。其次，将复合观测器与无模型预测控制结合，提升了系统在参数失配及采样噪声工况下的性能。最后，搭建MATLAB/Simulink仿真和样机实验系统，验证所提方法的有效性和正确性。

关键词: 无模型预测控制参数失配卡尔曼滤波扩张状态观测器模块化多电平换流器

DOI：10.19783/j.cnki.pspc.250860

分类号:

基金项目:国家自然科学基金项目资助(52277208)；中国石油重大科技攻关专项资助(2023ZZ31YJ01，2023ZZ31YJ03)

Model-free predictive control strategy of MMC based on a Kalman filtering-quasi-resonant extended state observer

LIANG Bei¹,MA Wenzhong¹,WANG Yusheng²,MENG Lingtong³,SONG Shuguang¹,ZHENG Shaotong¹

1. College of New Energy, China University of Petroleum (East China), Qingdao 266580, China; 2. PetroChina Planning and Engineering Institute, Beijing 100083, China; 3. PetroChina Tarim Oilfield Company, Korla 841000, China

Abstract:

Traditional finite-control-set model predictive control is widely applied to complex nonlinear systems such as modular multilevel converters (MMC) due to its capability for multi-objective control. However, its performance deteriorates under parameter mismatch and sensor noise conditions. To address these issues, this paper proposes a model-free predictive control strategy for MMC based on a Kalman filtering-quasi-resonant extended state observer (KF-QRESO) to enhance system robustness against parameter mismatch and sampling disturbances. First, the discrete mathematical model of MMC under parameter mismatch is analyzed, and a composite KF-QRESO observer is constructed. The Kalman filter (KF) is used to suppress sampling noise, while the QRESO accurately estimates periodic AC state variables and compensates them within the KF state equations. Then, the composite observer reduces the parameter dependence of the control system, achieving precise estimation. The tracking ability and stability of the observer for periodic signals are also studied. Next, the composite observer is integrated with model-free predictive control to improve performance under parameter mismatch and sampling noise conditions. Finally, MATLAB/Simulink simulations and prototype experiments validate the method’s effectiveness and correctness.

Key words: model-free predictive control parameter mismatch Kalman filtering extended state observer modular multilevel converter

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