Search Results for author:
Found 4 papers, 0 papers with code
When is gray-box modeling advantageous for virtual flow metering?
The experiments are conducted with synthetic data where properties of the underlying data generating process are known and controlled.
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
Nonlinear model predictive control (NMPC) is an efficient approach for the control of nonlinear multivariable dynamic systems with constraints, which however requires an accurate plant model.
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control
In this paper we introduce a new algorithm to explicitly consider time-invariant stochastic uncertainties in optimal control problems.
Stochastic data-driven model predictive control using Gaussian processes
Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear controlsystems with constraints.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
