A Sampling Time Study for Model Predictive Control in Induction Motor Using Processor-In-Loop Verification
Lecture notes in electrical engineering(2022)
摘要
This paper examines the impact of sampling time variation on Model Predictive Control (MPC) when it is applied to Induction motors (IM). Sampling time is a vital element in digital controllers, and selecting the appropriate value can be challenging. MPC is a model-dependent digital controller, which is highly affected by sampling time as well as the discretization technique employed by the hardware implementation. Hardware typically uses a discrete controller, and the sampling time and the discretisation method are important factors in the performance. The study proposes utilizing PIL verification with a variety of discretisation methods and sampling times with MPFOC to monitor the performance of the microcontroller. The optimal sampling time is selected by using a numerical optimization method within several test results. The optimisation results found that using a 25 $$\mu $$ s sampling time with the proposed discretisation method will achieve an enhancement of 16% in total in terms of calculation time and accuracy as compared with conventional Euler’s method.
更多查看译文
关键词
model predictive control,induction motor,sampling time study,processor-in-loop
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要