TR2019-052
H Infinity Loop-Shaped Model Predictive Control with Heat Pump Application
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- "H Infinity Loop-Shaped Model Predictive Control with Heat Pump Application", European Control Conference (ECC), DOI: 10.23919/ECC.2019.8796158, June 2019, pp. 2386-2393.BibTeX TR2019-052 PDF
- @inproceedings{Bortoff2019jun,
- author = {Bortoff, Scott A. and Schwerdtner, Paul and Danielson, Claus and Di Cairano, Stefano},
- title = {H Infinity Loop-Shaped Model Predictive Control with Heat Pump Application},
- booktitle = {European Control Conference (ECC)},
- year = 2019,
- pages = {2386--2393},
- month = jun,
- publisher = {IEEE},
- doi = {10.23919/ECC.2019.8796158},
- url = {https://www.merl.com/publications/TR2019-052}
- }
,
- "H Infinity Loop-Shaped Model Predictive Control with Heat Pump Application", European Control Conference (ECC), DOI: 10.23919/ECC.2019.8796158, June 2019, pp. 2386-2393.
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MERL Contacts:
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Research Area:
Abstract:
In this paper we derive a formulation for Model Predictive Control (MPC) of linear time-invariant systems based on H infinity loop-shaping. The design provides an optimized stability margin for problems that require state estimation. Input and output weights are designed in the frequency domain to satisfy steady-state and transient performance requirements, in lieu of conventional MPC plant model augmentations. The H infinity loop-shaping synthesis results in an observer-based state feedback structure. Using the linear state feedback law, an inverse optimal control problem is solved to design the MPC cost function, and the H infinity state estimator is used to initialize the prediction model at each time step. The MPC inherits the closed-loop performance and stability margin of the loopshaped design when constraints are inactive. We apply the methodology to a multi-zone heat pump system in simulation. The design rejects constant unmeasured disturbances and tracks constant references with zero steady-state error, has good transient performance, provides an excellent stability margin, and enforces input and output constraints.
Related News & Events
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NEWS Scott Bortoff gave Mercer Distinguished Lecture at Rensselaer Polytechnic Institute Date: September 25, 2019
Where: Rensselaer Polytechnic Institute (RPI), Troy, NY
MERL Contact: Scott A. Bortoff
Research Areas: Control, Multi-Physical ModelingBrief- The seminar, entitled “HVAC System Control and Optimization,” was part of the Mercer Distinguished Lecture Series in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI), Troy, NY. Given on Wednesday September 25, 2019, it focused on the systems engineering and control issues associated with highly integrated Heating, Ventilation and Air Conditioning Systems for low and zero energy buildings.
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NEWS MERL researchers presented more than 8 papers in European Control Conference, ECC 2019 Date: June 25, 2019 - June 28, 2019
Where: Naples, Italy
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Control, Machine Learning, OptimizationBrief- The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.