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چکیده
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The most challenging aspect of utilizing model predictive controllers (MPCs), particularlythose involving power electronic applications, is the extraction of a model that accuratelyrepresents the behavior of the studied system. Concerning the use of power electronicapplications, as long as an MPC is used, adjusting the controller parameters brings dif-ficulties. In addition, as the number of elements increases, it becomes harder to get thebest control law out of the model. To do away with the need for model extraction, thisstudy presents an offline data-driven approach in conjunction with the MPC that can opti-mally adjust the MPC parameters based on the iterative feedback tuning (IFT) algorithmcalled the iterative feedback predictive controller (IFPC). The proposed method eliminatesconcerns regarding selecting an optimal number of algorithm iterations, thereby reducingoperating costs, by introducing a modified IFT called feedback-based IFPC (FIFPC) whilesimultaneously achieving optimal MPC parameters. The proposed method is applied to aconstant voltage load (CVL) connected less-than-ideal buck converter, that is, one withperturbed filter elements and variable loads. A robust stability analysis (RSA) is performedunder normal operating conditions to investigate the robustness behavior of the proposedcontroller. Simulation studies are presented to evaluate the proposed controller underdifferent scenarios, such as step and abrupt load changes and measurement noise, com-pared with the well-known model-based and data-enabled predictive controller (DeePC)approaches in the MATLAB/Simulink environment.
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