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Composite model predictive control for the boost converter and two-phase interleaved boost converter

Muhammad Adnan SamadKey Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, ChinaYuanqing XiaKey Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, ChinaTayyab ManzoorSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaKashif MehmoodDepartment of Electrical Engineering, Fergana Polytechnic Institute, Fergana, UzbekistanAdeel SaleemDepartment of Electrical Engineering, Fergana Polytechnic Institute, Fergana, UzbekistanAhmad H. MilyaniDepartment of Electrical and Computer Engineering, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi ArabiaAbdullah Ahmed AzhariThe Applied College, King Abdulaziz University, Jeddah, Saudi Arabia
ABI

Аннотация

This article compares the conventional model predictive control (MPC) and active disturbance rejection control (ADRC) with a novel MPADRC technique for controlling a non-minimum phase behavior in the DC–DC boost converter. The control of the boost converter is challenging as it is nonlinear, and it shows non-minimum phase behavior in a continuous conduction mode (CCM). Moreover, in this article, the comparison is presented for the boost converter and the two-phase interleaved boost converter using MPC and ADRC, and the effectiveness of the interleaving technique is shown. Finally, it is proved that the interleaving method has much more efficiency and less output ripple than the simple boost converter. To conclude, a novel technique has been introduced that combines both the techniques, that is, MPC and ADRC, in the outer and inner loop with a boost converter, respectively, and the response is clearly the best when compared to the said techniques individually. The overall impact of this technique includes the advantages of both the techniques, that is, the use of MPC allows us to optimize the current value by predicting the future values, and the use of ADRC ensures that the disturbance factor is well tackled and cancels the effect caused by all the disturbances including ignored quantities as well.

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