Fuzzy model predictive control of a DC-DC boost converter based on non-linear model identification

Robert Baždarić, Drago Matko, Aleš Leban, Danjel Vončina and Igor Škrjanc

Mathematical and Computer Modelling of Dynamical Systems, 2017, vol. 23, issue 2, 116-134

Abstract: We present a novel method for the fuzzy control of a DC-DC boost converter based on a new approach to modelling the converter using Takagi–Sugeno (T-S) fuzzy identification. Two grades of identification result in a global model of a non-linear dynamical system and its finite impulse response model (FIRM) expression, which is therefore applicable in various model predictive control (MPC) standard methods with constraints. The successful simulation and experimental results shown in this study indicate the robustness and demonstrate stable operation of the DC-DC converter, even in the dynamic exchange of the discontinuous conduction mode (DCM) and the continuous conduction mode (CCM) with the preservation of a similar transient time. Although the study was primarily conducted on a hybrid simulation model of the DC-DC boost converter, the presented method is insensitive to the complexity of the physical process, as it suggests identified model-based control and emphasizes a new, general approach to pulse energy converter (PEC) controls. The statement is pursued with the subsequent application to the physical system of the converter. Furthermore, it underlines the method’s consideration of real-time processing and its final online simplicity.

Date: 2017
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DOI: 10.1080/13873954.2016.1232283

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