.png){kind=icon}
.png){kind=icon}
Return
Finite-Set Model-Based Predictive Control for Flying-Capacitor Converters: Cost Function Design and Efficient FPGA Implementation
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2012.2223707.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, there has been an increase in the use of finite-set model-based predictive control (FS-MBPC) for power-electronic converters. However, the computational burden for this control scheme is very high and often restrictive for a good implementation. This means that a suitable technology and design approach should be used. In this paper, the implementation of FS-MBPC for flying-capacitor converters in field-programmable gate arrays (FPGAs) is discussed. The control is fully implemented in programmable digital logic by using a high-level design tool. This allows us to obtain very good performances (both in control quality, speed, and hardware utilization) and have a flexible, modular control configuration. The good performance is obtained by exploiting the FPGA's strong points: parallelism and pipelining. Furthermore, an improved cost function for the voltage control of the flying capacitors is proposed in this paper. Typical cost functions result in tracking control for the flying-capacitor voltages, although this does not correspond with the desired system behavior. The improved cost function offers a capacitor voltage control that corresponds more closely with the desired behavior and adds a limitation on the capacitor voltage deviation. Furthermore, the selection of the weight factor in the cost function becomes less critical.
Keywords:
Field-prgrammable gate array (FPGA) implementation
flying-capacitor converters (FCCs)
model-based predictive control (MBPC)
predictive control
programmable digital hardware
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W