Chen, JunBadawi, RanyaDas, ManoharHorvath, TamasArefifar, Seyed Ali2025-07-112025-07-112025-01-01https://hdl.handle.net/10323/18810Model Predictive Control (MPC) has been gaining popularity as a time-domain control method for power converters. Event-triggered MPC has been explored as a method to reduce the computational burden of enumeration-based MPC. Existing literature reports MPC's successful use in power converter applications but does not widely explore the use of event-triggered control in similar applications. This investigation proposes a method to utilize event-triggered model predictive control (ET-MPC) in DC-to-DC power converters to achieve significant computational savings by reducing the frequency of control updates to only when needed. The method proposed solves an optimal control problem (OCP) to generate an optimal actuating value only when an event is triggered as opposed to solving the OCP at every time step. The purpose is to reduce the computational load of an enumeration-based time-triggered MPC over a defined time-frame. The novelty of this method lies in the selection of the actuating control signal, where the control actions are selected from the optimal switching sequence as opposed to upholding the last value of the optimal actuating value as reported in prior literature. A Kalman Filter-based estimator is added to the control system to ensure accurate voltage tracking during model mismatch which commonly occurs during load transients. In this work, ET-MPC is successfully implemented on both a DC-to-DC boost and a buck converter showing significant computational savings. The performances of the conventional time-triggered MPC and the proposed event-triggered MPC are compared through simulation. The effect of the event-trigger threshold is evaluated as a tuning parameter to balance computation and control performanceDisturbance observerEvent-triggeredKalman FilterModel Predictive ControlPower converters