[HTML][HTML] Closed-loop optimisation of neural networks for the design of feedback policies under uncertainty
Solving model predictive control (MPC) problems online can be computationally intractable,
especially when considering uncertainty and nonlinear systems. One approach to avoid this …
especially when considering uncertainty and nonlinear systems. One approach to avoid this …
Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study
The online implementation of model predictive control for constrained multivariate systems
has two main disadvantages: it requires an estimate of the entire model state and an …
has two main disadvantages: it requires an estimate of the entire model state and an …
[PDF][PDF] Learning convex objectives to reduce the complexity of model predictive control
For large systems that consider uncertainty the online solution of model predictive control
problems can be computationally taxing, and even infeasible. This can be offset by using a …
problems can be computationally taxing, and even infeasible. This can be offset by using a …
[PDF][PDF] Implementing Neural Network-based Control Policy for Nonlinear Compressor Surge Control
H Dawlatshahi - 2024 - ntnuopen.ntnu.no
Denne masteroppgaven utforsker implementeringen av en kontrollpolitikk basert på nevrale
nettverk for ̊a evaluere ytelsen til kontrollsystemet. Tradisjonelle modell-prediktiv kontroll …
nettverk for ̊a evaluere ytelsen til kontrollsystemet. Tradisjonelle modell-prediktiv kontroll …