| |
An Iterative Learning Based Compensation in Model Predictive Control for DC/DC Boost Converter
| [View]
[Download]
|
| Author(s) |
Yuan LI, Subham SAHOO, Zhihao LIN, Yichao ZHANG, Tomislav DRAGICEVIC, Frede BLAABJERG |
| Abstract |
Attributed to the increased processingpower of modern microprocessors, model predictive control(MPC) for power converters is gaining more attention.However, the non-minimum phase behavior in DC/DCboost converters complicates the design of model predictivecontrol. When controlling the output voltage directly, itfails to track the reference with short prediction horizons,nevertheless, long prediction horizons cause a heavycomputational burden. Although controlling the inductorcurrent is a feasible option with a short prediction horizon,the control accuracy of the output voltage cannot beguaranteed. To address this issue, this work introducesa compensation term into the difference equation of theinductor current. Then the proportion of the compensationterm is designed with an iterative learning method to improvethe control accuracy. Finally, the results indicate theproposed method can ensure a good control performancewith different load occasions. |
| Download |
| Filename: | 0385-epe2023-full-11282817.pdf |
| Filesize: | 778 KB |
|
| Type |
Members Only |
| Date |
Last modified 2023-09-24 by System |
| |
 |