Name
Abstract | ||
|---|---|---|
In this paper, a novel historian data based predictive control strategy is presented and used to control a water distribution network simulated using the EPANET software. The control actions are computed based on past historian data. The historian stores closed loop operation data of the process with different controllers used in the past. The predictive controller computes the current control actions as a weighted sum of past control actions so that a performance cost over a prediction horizon is minimized. This predictive strategy does not need an explicit model of the process and it is well suited to control applications of large and complex processes such as water distribution networks. To limit the computational burden, only a subset of the past control actions in the historian are considered in current control computations. This subset is comprised of closed loop trajectories starting from a initial state close to the current state of the process. Furthermore, other parameters different from the initial state can be considered when choosing the historian subset (e.g., the set point values). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/ECC.2018.8550602 | 2018 EUROPEAN CONTROL CONFERENCE (ECC) |
Field | DocType | Citations |
Control theory,Optimal control,Computer science,Control theory,Model predictive control,Distribution networks,Software,Trajectory,Computation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose R. Salvador | 1 | 0 | 0.68 |
| David Muñoz de la Peña | 2 | 293 | 24.98 |
| Daniel R. Ramírez | 3 | 0 | 0.68 |
| Alamo Teodoro | 4 | 307 | 36.82 |