控制理论(社会学)
模型预测控制
稳健性(进化)
鲁棒控制
计算机科学
观察员(物理)
航程(航空)
控制系统
控制工程
工程类
控制(管理)
人工智能
电气工程
生物化学
化学
物理
量子力学
航空航天工程
基因
作者
Xicai Liu,Libing Zhou,Jin Wang,Xiaonan Gao,Zhixiong Li,Zhiwei Zhang
出处
期刊:IEEE Transactions on Power Electronics
[Institute of Electrical and Electronics Engineers]
日期:2020-10-01
卷期号:35 (10): 10778-10788
被引量:65
标识
DOI:10.1109/tpel.2020.2980930
摘要
In model predictive control, mathematical model of the system is used to predict the value of state variables. Control performances of model predictive control suffer from parameter mismatches and model uncertainties. Steady-state errors will exist due to inaccurate predictions. This article presents a simple predictive current control strategy for robustness improvement of finite control set-model predictive control, with which steady-state errors under parameter mismatches can be eliminated. Neither disturbance observer nor explicit solution of compensation voltage is needed in the proposed control strategy. A cost function is newly designed, which is in proportional-integral form. Moreover, the integral action is only activated in a predefined range, which facilitates the design of the integral coefficients. The accumulated error is not simply included but weighted with the sampling time. In this way, a careful selection of the number of integral terms to be included in the cost function is not required. Experimental results demonstrate superior robustness of the proposed current control strategy to that of conventional predictive current control against parameter uncertainties.
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