Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer

Rongchao Niu, Hongyu Zhang () and Jian Song
Additional contact information
Rongchao Niu: Xi’an Institute of Applied Optics, Xi’an 710065, China
Hongyu Zhang: School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Jian Song: School of Automation, Northwestern Polytechnical University, Xi’an 710129, China

Energies, 2023, vol. 16, issue 3, 1-16

Abstract: Due to the nonminimum phase characteristics and nonlinearity of boost converters, the control design is always a challenging issue. A novel model predictive control strategy is proposed for the boost converter in this work. First, the Super-Twisting algorithm is applied to current control, and the input–output plant for voltage control is derived based on the linearization technique. All the model uncertainties are defined as lumped disturbances, and a generalized proportional integral observer is designed to estimate the lumped disturbance. Second, a composite predictive approach is developed on the basis of the predictive model and disturbance estimations. By solving the cost function directly, the optimal control law is derived explicitly. Lastly, the effectiveness of the proposed control strategy is verified by both simulation and experimental results.

Keywords: model predictive control; DC–DC boost converter; generalized proportional integral observer; offset-free tracking; disturbance estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1245/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1245/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1245-:d:1045168

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().