Event-triggered boost converter model predictive control with Kalman filter

Event-triggered control has been gaining popularity as a method to reduce the computational burden of model predictive control (MPC). Existing literature reports its successful use in power converter applications. In our survey, event-triggered model predictive control (ET-MPC) is used to improve the computational performance of an enumeration-based MPC controlled boost converter. ET-MPC 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, and hence reduce the computational load. In addition, a Kalman Filter-based estimator is added to the control system to ensure accurate voltage tracking even in the presence of model mismatch, which commonly occurs during load transients. The novelty of this work 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. Extensive simulation evaluations are conducted to compare the performance of conventional time-triggered MPC and the proposed event-triggered MPC, where the event-trigger threshold is used as a tuning parameter to balance computation and control performance.