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Found 6 papers, 2 papers with code
Improved uncertainty quantification for neural networks with Bayesian last layer
Bayesian neural networks are an approach to address this limitation.
On the Practical Design of Tube-Enhanced Multi-Stage Nonlinear Model Predictive Control
Tube-enhanced multi-stage nonlinear model predictive control is a robust control scheme that can handle a wide range of uncertainties with reduced conservatism and manageable computational complexity.
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultivations with intermittent bolus feeding.
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model.
On the relationship between data-enabled predictive control and subspace predictive control
Data-enabled predictive control (DeePC) is a recently proposed approach that combines system identification, estimation and control in a single optimization problem, for which only recorded input/output data of the examined system is required.
Probabilistic performance validation of deep learning-based robust NMPC controllers
Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations.
