.png){kind=icon}
返回
A Two-Step Event-Triggered-Based Data-Driven Predictive Control for Power Converters
.png){kind=icon}
PRE
AI
DOI：10.1109/TIE.2024.3360630.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, event-triggered model predictive control (MPC) is addressed as a promising and powerful technique for power converter systems. The core idea behind this technique is to explicitly provide a feasible solution to reduce switching actions. However, it is prone to suffer from unknown uncertainties and large tracking error that hinder its potential when medium to high accuracy is desired. To tackle the difficulties caused by the uncertainties and tracking error, our work takes a step forward toward addressing a modified data-driven event-triggered model-free predictive control architecture leveraging a two-step event-triggered protocol subject to parametric uncertainties. Meanwhile, an integral error term is embedded into this proposal so as to enhance the tracking performance under low switching frequency (SF) operation. Compared to previous studies, we show that this modification not only endows with uncertainties and SF as well as tracking error attenuating capabilities but also inspires new works in the intersection of event-driven control technique and MPC theory, without requiring a priori knowledge of model dynamics and weighting factors. Finally, numerical examples illustrate the interest and efficacy of this proposal.
Keyword:
Finite control-set model predictive control (FCS-MPC)
low switching frequency (SF)
power converter systems
tracking error
two-step event-triggered (ET) mechanism
期刊
IF:
7.2
论文数:
1.8W
被引数:
9.8W
机构
引用论文
.png){kind=icon}
Interleaved Model Predictive Control for Three-Level Neutral-Point-Clamped Dual Three-Phase PMSM Drives With Low Switching Frequencies低开关频率三电平中点箝位双三相永磁同步电机交错模型预测控制
OAAI
OAAI分享收藏A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems一类离散时间非线性系统的数据驱动控制新方法
PREAI
分享收藏分享收藏
查看更多内容