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May 9, 2026Journal of Dynamic Systems Measurement and Control

Event-Triggered Model Predictive Control of a Buck Converter with Kalman Filter State Estimation

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Authors

WYWanqun YangRBRanya BadawiJCJ Chen

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Overview

Randomized trial demonstrates improved control efficiency in buck converters, suggesting less computational load.

Key Points

  • The aim is to develop an event-triggered model predictive control strategy to enhance the performance of buck converters while minimizing computational demands.
  • A four-mode discrete-time model for the buck converter was established.
  • An event-triggered mechanism only evaluates optimal switching sequences when voltage deviations exceed thresholds.
  • A Kalman filter was integrated to estimate load disturbances.
  • ET-MPC achieves up to 94% reduction in computational effort while maintaining similar transient response.
  • Steady-state error was low and switching frequency remained acceptable in simulations.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69fed19ab9154b0b8287904dhttps://doi.org/10.1115/1.4071872
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