Title
Superposed Compensation Strategy to Optimize Load/line Transient Response and Reference Tracking for Discontinuous Conduction Mode Boost Converter
Abstract
For boost converter operating in the discontinuous conduction mode (DCM), feedback and feedforward compensators are widely used to improve the converter performance. However, it is relatively difficult to optimize load transient response (LoTR), line transient response (LiTR), and reference tracking speed (RTS) simultaneously, since the optimizations require different compensators that are incompatible. In order to solve the issue, a superposed compensation strategy is proposed in this paper, which consists of a feedback compensator and two feedforward compensators. Each compensator is tuned according to an objective transfer function, which optimizes LoTR, LiTR, and RTS. The outputs are summed as duty cycle according to the linear superposition principle. Compatibility of the compensators is improved by designing the feedforward compensators to adapt to the feedback compensator. Furthermore, based on the closed-loop model, design rules for the objective transfer functions are given to minimize the influences of the sample-and-hold effect and calculation delay, which are intrinsic in a digital controller. Finally, converter's LoTR, LiTR, and RTS are simultaneously optimized, which is proven by closed-loop magnitude–frequency plots, state trajectory analyses, and experimental results.
Year
DOI
Venue
2019
10.1109/tii.2018.2871359
IEEE Transactions on Industrial Informatics
Keywords
Field
DocType
Transfer functions,Feedforward systems,Delays,Voltage control,Pulse width modulation,Transient response,Switches
Boost converter,Transient response,Duty cycle,Computer science,Control theory,Pulse-width modulation,Control engineering,Transfer function,Load line,Digital control,Feed forward
Journal
Volume
Issue
ISSN
15
5
1551-3203
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Run Min1111.86
Xiaofeng Zhang200.68
Dian Lyu321.05
Linkai Li421.05
Qiaoling Tong5154.03
Xue-cheng Zou615028.50
Zhenglin Liu7533.97