Robust Model Predictive Control of DC-DC Floating Interleaved Boost Converter With Multiple Uncertainties

DC-DC Floating Interleaved Boost Converter (FIBC) is recently introduced for converting low-level voltage generated by a renewable energy source to high-level voltage required for AC inverters. Although a desired voltage is expected at the output, designing a proper voltage gain for FIBC is challenging due to different types of uncertainties. For instance, the voltage generated by the energy source and, therefore, the input voltage of FIBC may change by a variety of parameters including external load. Furthermore, parametric uncertainty and measurement noise are other sources which can affect the control procedure. As a result, voltage gain for a fixed switching duty cycle may be uncertain. It demands a robust approach to guarantee the control performance under uncertainties without the need for individually tuning controller for each single converter. In this work, a robust model predictive control is employed to regulate the output voltage at the desired level despite the existing uncertainties. Controller parameters are fixed for any FIBC within the uncertainty range and further tuning is not required for individual converters. In addition, unlike the conventional controllers, the suggested controller is able to handle input-output constraints. Performance of the suggested controller is investigated through simulations carried out in MATLAB and the superiority of the proposed approach is verified over non-robust model predictive framework.