Name
Abstract | ||
|---|---|---|
In this paper, we optimize vapor compression system (VCS) power consumption through the application of a novel proportional–integral extremum-seeking controller (PI-ESC) that converges at the same timescale as the process. This extremum-seeking method uses time-varying parameter estimation to determine the local gradient in the map from manipulated inputs to performance output. Additionally, the extremum-seeking control law includes terms proportional to the estimated gradient, which requires subsequent modification of the estimation routine in order to avoid bias. The PI-ESC algorithm is derived and compared to other methods on a benchmark example that demonstrates the improved convergence rate of PI-ESC. PI-ESC is applied to the problem of compressor discharge temperature setpoint selection for a VCS such that power consumption is driven to a minimum. A physics-based simulation model of the VCS is used to demonstrate that with PI-ESC, convergence to the optimal operating point occurs faster than the bandwidth of typical disturbances—enabling application of extremum-seeking control to VCSs in environments under realistic operating conditions. Finally, experiments on a production room air conditioner installed in an adiabatic test facility validate the approach in the presence of significant noise and actuator and sensor quantization. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCST.2018.2882772 | IEEE Transactions on Control Systems Technology |
Keywords | DocType | Volume |
Adaptive control,energy,extremum seeking control,optimization,vapor compression system (VCS) | Conference | 28 |
Issue | ISSN | Citations |
2 | 1063-6536 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel J. Burns | 1 | 9 | 2.82 |
| Christopher Laughman | 2 | 3 | 1.75 |
| M. Guay | 3 | 283 | 41.27 |