This paper introduces the principles of current predictive control-based PWM inverter, analyses some existing problems, and proposes improvement method.
基于电流预测控制的PWM逆变器的基本原理，提出了改进方法。仿真结果显示，通过这些方法可获得更好的电流控制性能。
Through the analysis of SAPF in time domain, the internal model of current predictive control is obtained and the current loop of SAPF is studied and realized.
通过对并联有源电力滤波器的时域分析，得到了电流预测控制的内部模型结构，并根据预测控制理论对并联有源电力滤波器的电流控制环进行了研究和实现。
They are based on a predictive instantaneous current PWM control.
它们是基于预测瞬时电流的PWM控制。
The objective of the current study is to design and implement a proper algorithm based on NN generalized predictive control (GPC).
当前研究的目标是设计并实现一个基于神经网络广义预测控制的合适算法。
This paper presents a new self-adaptive current controller based on hysteresis control and predictive control.
文章提出了一种基于滞环控制和预测控制的自适应电流控制器。
Predictive current control is adopted to obtain compensation currents, which has good control precision and compensation performance.
补偿电流采用预测电流控制，获得了较好的补偿特性和控制精度。
Based on the traditional control on PWM rectifier, space vector PWM predictive current controller is presented using the switching function model.
在传统的PWM整流控制的基础上，论文利用开关函数建模法，提出了基于预测电流的PWM电压空间矢量控制方法。
DSTATCOM is designed with double loop control method of predictive current and 3-level inverter for its main circuit.
DSTATCOM采用预测电流控制的双环控制方法和三电平逆变器的主电路设计。
For an industrial plant with high great lag, the current control strategy is Smith predictive control, or internal model control, both of them are using continuous models.
对于带有严重纯滞后的工业对象，目前的控制策略是史密斯预估控制，或者是内模控制，他们都要使用连续域的数学模型。
Model predictive control (MPC) refer to a class of control algorithms that optimize the current input by predicting the future behavior of system based on system dynamic model.
模型预测控制(MPC)是一种根据系统动态模型和历史信息，通过对系统未来行为的预测来优化当前输入的控制策略。
Model predictive control (MPC) refer to a class of control algorithms that optimize the current input by predicting the future behavior of system based on system dynamic model.
模型预测控制(MPC)是一种根据系统动态模型和历史信息，通过对系统未来行为的预测来优化当前输入的控制策略。
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