针对新能源汽车充电（G2V）三相整流器为研究对象,研究基于模型预测控制（model predictive control, MPC）的充电控制策略。传统的MPC算法需要准确的系统模型参数,而当控制器中使用的模型参数与主电路实际参数不匹配时,控制性能可能发生恶化,影响整流器充电控制性能。针对此问题,该文将系统参数不匹配作为扩张状态观测器（extended state observer, ESO）扩张出来的扰动项而进行估计,并将扰动进行补偿,从而设计一种基于ESO的MPC充电控制策略。该方法仅使用了系统的输入和输出数据,而不需要精确的系统模型,因此即使模型参数不匹配时,ESO也能够将不匹配项作为扰动而对预测电流进行准确估计,从而提高MPC对参数变化及不匹配的鲁棒性。仿真与实验结果验证了该方法的可行性和有效性。
Abstract
This paper focuses on the model predictive charging control strategy for renewable energy vehicle charging （G2V） rectifier. The traditional model predictive control （MPC） algorithm requires accurate system model parameters. Therefore, if the model parameters used in the controller are inaccurate, the performance may deteriorate, affecting the charging control performance of the rectifier. To address this issue, the MPC based on the extended state observer （ESO） for G2V charging rectifier is proposed. The parameter mismatch is regarded as the extended disturbance term and estimated by the extended state observer （ESO）. The proposed method only utilizes system input and output data and does not need accurate system parameters. When there exists a parameter mismatch, it can be regarded as a disturbance and be estimated by ESO, thus achieving an accurate estimation of the predicted current. Therefore, the robustness of the controller against the parameter mismatch is enhanced. Simulation and experimental results verify the feasibility and effectiveness of the proposed MPC based on ESO.
关键词
模型预测控制 /
新能源汽车 /
扰动抑制 /
鲁棒性 /
扩张状态观测器 /
模型参数不匹配
Key words
model predictive control /
renewable energy vehicles /
disturbance rejection /
robustness /
extended state observer /
model parameters mismatch
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基金
内蒙古自然科学基金（2020BS05027）; 内蒙古自治区科技重大专项（2020ZD0014）; 内蒙古自治区高等学校青年科技英才支持计划（NJYT22082）; 自治区直属高校基本科研业务费项目（JY20220092）
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