Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive Control

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In this presentation, we showed how to use a recurrent neural network-based method to address joint chance-constrained stochastic model predictive control (SMPC) problems.

The presented method can handle a nonlinear joint chance-constrained SMPC problem with high computational efficiency as shown in the case study.

  1. Shu-Bo Yang
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