Proportional-Integral Extremum Seeking for Vapor Compression Systems

    •  Burns, D.J., Laughman, C.R., Guay, M., "Proportional-Integral Extremum Seeking for Vapor Compression Systems", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2018.2882772, ISSN: ., December 2018.
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      • @article{Burns2018dec,
      • author = {Burns, Daniel J. and Laughman, Christopher R. and Guay, Martin},
      • title = {Proportional-Integral Extremum Seeking for Vapor Compression Systems},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2018,
      • month = dec,
      • doi = {10.1109/TCST.2018.2882772},
      • issn = {.},
      • url = {}
      • }
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In this paper, we optimize vapor compression system 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 vapor compression system such that power consumption is driven to a minimum. A physicsbased simulation model of the vapor compression system 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 vapor compression systems 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.