ahodzic7/waterlab-estimator

Stochastic Model Predictive Control for Combined Sewer Overflows in Urban Drainage Networks

This repo has the objective of covering the "code" produced as part of Adis and Caspers Master Thesis at Aalborg University. The repo may include snippets of code from a previous project: https://github.com/dmicha16/waterlab_mpc

Futhermore some code may have been provided by our supervisors:

• Krisztian Mark Balla - LinkedIn
• Carsten Skovmose Kallesøe - LinedIn

Masters Thises at Programme: Control and Automation

The thises plan includes the following tasks

1. System identification: System identification (Grey-box) of gravity pipes, relying on the physical laws described by the Saint-Venant hyperbolic Partial Differential Equations.

2. State estimation: Full state observability in sewer applications is typically not available, hence only some subsets of states are measured.

3. Stochastic Model Predictive Control: Although standard MPC methods offer a certain degree of robustness, stochastic MPC is a natural extension to deal with the uncertainties systematically.

4. Multi-objective optimization: Control in UDNs is typically proposed as a multi-criteria optimization with conflicting objectives, where operational objectives and the choice of weighing parameters needs to be considered for the optimization problem.