Abstract This paper presents an event-triggered model predictive control (ET-MPC) strategy for a DC-DC buck converter to reduce computational burden while maintaining high dynamic performance. A four-mode discrete-time model is established to accurately capture the converter switching behavior, and a Kalman filter is integrated into control framework to estimate load disturbances and improve control accuracy. Unlike conventional time-triggered MPC, which solves an optimization problem at every time step, the proposed ET-MPC evaluates the optimal switching sequence only when a voltage deviation exceeds a predefined threshold. This mechanism significantly reduces the number of online optimizations while preserving regulation of output voltage. Simulation results demonstrate that ET-MPC achieves up to 94% reduction in computational effort with comparable transient response, low steady-state error, and acceptable switching frequency. The proposed controller enables an efficient real-time implementation of MPC for power converters under varying operating conditions.
Yang et al. (2026) studied this question.