Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

This research investigates the Adaptive Model Predictive Controller (AMPC) and Linear Parameter-Varying (LPV) control system for a direct current (dc-dc) buck-boost converter, considering the parameters’ uncertainty. The LPV model and the AMPC are explicitly constructed to perform a robust control design for the proposed dc-dc converter. The LPV model was created out of a set of linearized systems at different operating conditions to perform Linear Time-Invariant (LTI) models. Due to the dc-dc converter’s nonlinear characteristic, the performed LTI models might have declination, which the AMPC can perfectly address by adapting the prediction model for the changes in the operating conditions. The proposed AMPC control system was implemented in a simulation environment as well as in a real-time environment on an Arduino Mega 2560 microcontroller to test its robustness and quality. The proposed AMPC control system works well compared with some existing control system algorithms at different prediction horizons. Also, the comparison considers the designed Gain Scheduling Proportional Integral (G.S-PI) and the regular Model Predictive (reg-MPC) Controllers were implemented without using the LPV model to test their performance against the proposed converter’s parameters uncertainties.