A Passivity-Based Design of Cyber-Physical Building HVAC Energy Management Integrating Optimization and Physical Dynamics

  • Takeshi HatanakaEmail author
  • Tomohiro Ikawa
  • Na Li


This chapter investigates cyber-physical system (CPS) design for enhancing energy efficiency of heating, ventilation, and air-conditioning (HVAC) systems in a building consisting of multiple zones. We first present a thermodynamics model, called resistance–capacitance circuit model, where the inter-zone heat transfer is identified with current flow on an electrical circuit. We then formulate a set point optimization problem to balance the human comfort and energy saving while satisfying several constraints. We then design a CPS which integrates optimization dynamics based on primal-dual gradient dynamics and the physical dynamics with a local controller. The resulting CPS is then shown to be interpreted as an interconnection of passive systems. Accordingly, convergence of the room temperatures to the optimal solution and input–output stability are rigorously proved based on the passivity paradigm. The present framework is further extended to a scenario of co-optimizing energy management in multiple connected buildings. The present CPS is finally demonstrated on a simulator developed by combining a variety of software.


  1. 1.
    Afram A, Janabi-Sharif F (2014) Theory and applications of HVAC control systems—a review of model predictive control (MPC). Build Environ 72:343–355CrossRefGoogle Scholar
  2. 2.
    Aswani A, Master N, Taneja J, Culler D, Tomlin C (2012) Reducing transient and steady state electricity consumption in HVAC using learning-based model-predictive control. Proc IEEE 100(1):240–253CrossRefGoogle Scholar
  3. 3.
    Bernal W, Behl M, Nghiem T, Mangharam R (2012) MLE+: a tool for integrated design and deployment of energy efficient building controls. In: Real-time systems symposiuml work in progressGoogle Scholar
  4. 4.
    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University PressGoogle Scholar
  5. 5.
    Cherukuri A, Mallada E, Cortés J (2016) Asymptotic convergence of constrained primal-dual dynamics. Syst Control Lett 87:10–15MathSciNetCrossRefGoogle Scholar
  6. 6.
    Goyal S, Ingley H, Barooah P (2012) Zone-level control algorithms based on occupancy information for energy efficient buildings. In: Proceedings of 2012 American control conference, pp 3063–3068Google Scholar
  7. 7.
    Hatanaka T, Chopra N, Fujita M, Spong MW (2015) Passivity-based control and estimation in networked robotics. Springer-VerlagGoogle Scholar
  8. 8.
    Hatanaka T, Chopra N, Ishizaki T, Li N (2018) Passivity-based distributed optimization with communication delays using PI consensus algorithm. IEEE Trans Autom Control 63(12):4421–4428MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hatanaka T, Ikawa T and Okamoto D (2019) Remodeling of RC circuit building thermodynamics model with solar radiation based on a regularization-like technique. In: Proceedings of Asian control conference, pp 7–12Google Scholar
  10. 10.
    Hatanaka T, Zhang X, Shi W, Zhu M, Li N (2017) An integrated design of optimization and physical dynamics for energy efficient buildings: a passivity approach In: Proceedings of 1st IEEE conference on control technology and applications, pp 1050–1057Google Scholar
  11. 11.
    Hatanaka T, Zhang X, Shi W, Zhu M, Li N (2017) Physics-integrated hierarchical/distributed HVAC optimization for multiple buildings with robustness against time delays In: Proceedings of 56th IEEE conference on decision and control, pp 6573–6579Google Scholar
  12. 12.
    Hong Y, Hu J, Gao L (2006) Tracking control for multi-agent consensus with an active leader and variable topology. Automatica 42(7):1177–1182MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lawrence LSP, Nelson ZE, Mallada E, Simpson-Porco JW (2018) Optimal steady-state control for linear time-invariant systems. arXiv preprint arXiv:1810.03724
  14. 14.
    Lawrence LSP, Simpson-Porco JW, Mallada E (2018) The optimal steady-state control problem. arXiv preprint arXiv:1810.12892
  15. 15.
    Ma Y, Kelman A, Daly A, Borrelli F (2012) Predictive control for energy efficient buildings with thermal storage: modeling, stimulation, and experiments. IEEE Control Syst 32(1):44–64MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ma Y, Matusko J, Borrelli F (2015) Stochastic model predictive control for building HVAC systems: Complexity and conservatism. IEEE Trans Control Syst Technol 23(1):101–116CrossRefGoogle Scholar
  17. 17.
    Maasoumya M, Razmara M, Shahbakhti M, Vincentelli AS (2014) Handling model uncertainty in model predictive control for energy efficient buildings. Energy Build 77:377–392CrossRefGoogle Scholar
  18. 18.
    Mallada E, Zhao C, Low S (2014) Optimal load-side control for frequency regulation in smart grids. In: Proceedings of 52nd annual allerton conference on communication, control, and computing, pp 731–738Google Scholar
  19. 19.
    Morosan P-D, Bourdais R, Dumur D, Buisson J (2010) Building temperature regulation using a distributed model predictive control. Energy Build 42:1445–1452CrossRefGoogle Scholar
  20. 20.
    Oldewurtel F, Parisio A, Jones CN, Gwerder Gyalistras DM, Stauch V, Lehmann B, Morari M (2012) Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build 45:15–27Google Scholar
  21. 21.
    Privara S, Siroky J, Ferkl L, Cigler J (2011) Model predictive control of a building heating system: the first experience. Energy Build 43:564–572CrossRefGoogle Scholar
  22. 22.
    Shiltz DJ, Cvetkovic M, Annaswamy AM (2016) An integrated dynamic market mechanism for real-time markets and frequency regulation. IEEE Trans Sustain Energy 7(2):875–885CrossRefGoogle Scholar
  23. 23.
    Sturzenegger D, Gyalistras D, Semeraro V, Morari M, Smith RS (2014) BRCM Matlab toolbox: Model generation for model predictive building control. In: Proceedings of 2014 American control conference, pp 1063–1069Google Scholar
  24. 24.
    Takenaka H, Nakajima TY, Higurashi A, Higuchi A, Takamura T, Pinker RT, Nakajima T (2011) Estimation of solar radiation using a neural network based on radiative transfer. J Geophys Res 116:D08215CrossRefGoogle Scholar
  25. 25.
    Tsumura K, Baros S, Okano K, Annaswamy AM (2018) Design and stability of optimal frequency control in power networks: a passivity-based approach. In: Proceedings of 2018 European control conference, pp 2581–2586Google Scholar
  26. 26.
    US Dept. of Energy’s: EnergyPlus,
  27. 27.
    van der Schaft AJ (2000) L2-gain and passivity techniques in nonlinear control. 2nd edn. Springer-VerlagGoogle Scholar
  28. 28.
    Yuan S, Perez R (2006) Multiple-zone ventilation and temperature control of a single-duct VAV system using model predictive strategy. Energy Build 38:1248–1261CrossRefGoogle Scholar
  29. 29.
    Zhang X, Papachristodoulou A, Li N (2015) Distributed optimal steady-state control using reverse- and forward-engineering. In: Proceedings of 54th IEEE conference on decision and control, pp 5257–5264Google Scholar
  30. 30.
    Zhao C, Topcu U, Li N, Low S (2014) Design and stability of load-side primary frequency control in power systems. IEEE Trans Autom Control 59(5):1177–1189MathSciNetCrossRefGoogle Scholar
  31. 31.
    Zhang X, Shi W, Li X, Yan B, Malkawi A, Li N (2017) Decentralized and distributed temperature control via HVAC systems in energy efficient buildings. arXiv preprint arXiv:1702.03308

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of EngineeringTokyo Institute of TechnologyTokyoJapan
  2. 2.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

Personalised recommendations