<p>An adaptive event-triggered stochastic model predictive control (ET-SMPC) scheme based on error expectation is developed for the nonlinear Buck-Boost DC-DC converter system. The adaptive ET-SMPC scheme reduces the resource consumption by optimizing both the triggering frequency and computational burden. On the one hand, within the stochastic control framework, the proposed event-triggered mechanism introduces statistical characteristics into the triggering condition by incorporating the error expectation. On the other hand, an adaptive prediction horizon strategy and a terminal set are co-designed to further reduce the computational burden. In addition, necessary conditions are established to guarantee the recursive feasibility of the proposed scheme and the stability of the closed-loop system. Finally, numerical examples demonstrate that the adaptive ET-SMPC scheme is effective in the nonlinear DC-DC converter system.</p>

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Adaptive event-triggered stochastic MPC with an application to DC-DC converters: An error expectation approach

  • Ning He,
  • Wenqian Li,
  • Kai Ma

摘要

An adaptive event-triggered stochastic model predictive control (ET-SMPC) scheme based on error expectation is developed for the nonlinear Buck-Boost DC-DC converter system. The adaptive ET-SMPC scheme reduces the resource consumption by optimizing both the triggering frequency and computational burden. On the one hand, within the stochastic control framework, the proposed event-triggered mechanism introduces statistical characteristics into the triggering condition by incorporating the error expectation. On the other hand, an adaptive prediction horizon strategy and a terminal set are co-designed to further reduce the computational burden. In addition, necessary conditions are established to guarantee the recursive feasibility of the proposed scheme and the stability of the closed-loop system. Finally, numerical examples demonstrate that the adaptive ET-SMPC scheme is effective in the nonlinear DC-DC converter system.