A bio-inspired learning-and-reusing control strategy for multi-zone HVAC systems

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Heating, ventilation, and air conditioning (HVAC) systems are a major contributor to global building energy consumption; however, their control is complicated by inherent parametric uncertainties and time-varying disturbances. To address the limitations of conventional methods, a novel two-stage ”learning-and-reusing” framework was proposed, which fundamentally differs from existing methods by: (i) decoupling parameter learning from disturbance rejection to avoid the single-stage trade-off; (ii) using concurrent learning and an estimator for parameter identification under relaxed excitation conditions and input saturation. In the first learning stage, a concurrent-learning-based adaptive controller accurately identifies key thermodynamic parameters, such as the heat transfer coefficient and the cross-sectional areas of the wall, while simultaneously maintaining precise temperature regulation, thereby building a reliable knowledge base. In the second reusing stage, the identified model is used within a disturbance observer-based robust controller to precisely compensate for time-varying disturbances, such as fluctuating solar radiation and internal heat loads. Simulations on multi-zone building models validated the framework, demonstrating successful parameter convergence and superior robust tracking performance compared to conventional methods. This work offers an efficient, bio- inspired solution for intelligent building thermal management.

Keywords

heating, ventilation, and air conditioning systems / learning control / adaptive control / disturbance observer / nonlinear systems

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] N. Asim, M. Badiei, M. Mohammad, H. Razali, A. Rajabi, L. C. Haw, et al., Sustainability of heating, ventilation and air-conditioning (HVAC) systems in buildings-An overview, Int. J. Environ. Res. Public Health, 19 (2022), 1016. https://doi.org/10.3390/ijerph19021016

[2] X. Wang, Y. Bai, Z. Li, W. Zhao, S. Ding, Observer-based event triggering security load frequency control for power systems involving air conditioning loads, Electron. Res. Arch., 32 (2024), 6258-6275. https://doi.org/10.3934/era.2024291

[3] S. Sierla, H. Ihasalo, V. Vyatkin, A review of reinforcement learning applications to control of heating, ventilation and air conditioning systems, Energies, 15 (2022), 3526. https://doi.org/10.3390/en15103526

[4] S. Taheri, P. Hosseini, A. Razban, Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review, J. Build. Eng., 60 (2022), 105067. https://doi.org/10.1016/j.jobe.2022.105067

[5] A. Afram, F. Janabi-Sharifi, Theory and applications of HVAC control systems- A review of model predictive control (MPC), Build. Environ., 72 (2014), 343-355. https://doi.org/10.1016/j.buildenv.2013.11.016

[6] G. Serale, M. Fiorentini, A. Capozzoli, D. Bernardini, A. Bemporad, Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applica- tions and opportunities, Energies, 11 (2018), 631. https://doi.org/10.3390/en11030631

[7] B. Tashtoush, M. Molhim, M. Al-Rousan, Dynamic model of an HVAC system for control analysis, Energy, 30 (2005), 1729-1745. https://doi.org/10.1016/j.energy.2004.10.004

[8] H. Zhao, H. Cui, Y. Zhao, X. Dai, A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters, Electron. Res. Arch., 33 (2025), 3931-3949. https://doi.org/10.3934/era.2025174

[9] Q. Zhang, M. Mu, H. Ji, Q. Wang, X. Wang, An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator, Electron. Res. Arch., 31 (2023), 3791-3813. https://doi.org/10.3934/era.2023193

[10] M. Shen, T. Zhang, J. H. Park, Q. G. Wang, L. W. Li, Iterative proportional-integral interval estimation of linear discrete-time systems, IEEE Trans. Autom. Control, 68 (2023), 4249-4256. https://doi.org/10.1109/TAC.2022.3203226

[11] P. Michailidis, I. Michailidis, D. Vamvakas, E. Kosmatopoulos, Model-free HVAC control in buildings: A review, Energies, 16 (2023), 7124. https://doi.org/10.3390/en16207124

[12] X. Wang, B. Dong, Long-term experimental evaluation and comparison of advanced controls for HVAC systems, Appl. Energy, 371 (2024), 123706. https://doi.org/10.1016/j.apenergy.2024.123706

[13] X. Xin, Z. Zhang, Y. Zhou, Y. Liu, D. Wang, S. Nan, A comprehensive review of predictive control strategies in heating, ventilation, and air-conditioning (HVAC): Model-free VS model, J. Build. Eng., 94 (2024), 110013. https://doi.org/10.1016/j.jobe.2024.110013

[14] M. A. Adesanya, H. Obasekore, A. Rabiu, W. H. Na, Q. O. Ogunlowo, T. D. Akpenpuun, et al., Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy optimization: A TRNSYS-Python cosimulation approach, Expert Syst. Appl., 252 (2024), 124126. https://doi.org/10.1016/j.eswa.2024.124126

[15] Y. E. Jang, Y. J. Kim, J. P. S. Catalão, Optimal HVAC system operation using online learn- ing of interconnected neural networks, IEEE Trans. Smart Grid, 12 (2021), 3030-3042. https://doi.org/10.1109/TSG.2021.3051564

[16] J. Cho, Y. Heo, J. W. Moon, An intelligent HVAC control strategy for supplying com- fortable and energy-efficient school environment, Adv. Eng. Inf., 55 (2023), 101895. https://doi.org/10.1016/j.aei.2023.101895

[17] H. Wang, S. Wang, A disturbance compensation enhanced control strategy of HVAC systems for improved building indoor environment control when providing power grid frequency regulation, Renewable Energy, 169 (2021), 1330-1342. https://doi.org/10.1016/j.renene.2021.01.102

[18] T. Yu, Z. Zhang, Y. Li, W. Zhao, J. Zhang, Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery, Electron. Res. Arch., 31 (2023), 1570-1586. https://doi.org/10.3934/era.2023080

[19] M. Shen, X. Wang, J. H. Park, Y. Yi, W. W. Che, Extended disturbance-observer-based data-driven control of networked nonlinear systems with event-triggered output, IEEE Trans. Syst. Man Cybern. Syst., 53 (2023), 3129-3140. https://doi.org/10.1109/TSMC.2022.3222491

[20] S. A. A. Rizvi, A. J. Pertzborn, Z. Lin, Reinforcement learning based optimal tracking control under unmeasurable disturbances with application to HVAC systems, IEEE Trans. Neural Networks Learn. Syst., 33 (2022), 7523-7533. https://doi.org/10.1109/TNNLS.2021.3085358

[21] N. Nassif, A robust CO₂ -based demand-controlled ventilation control strategy for multi-zone HVAC systems , Energy Build., 45 (2012), 72-81. https://doi.org/10.1016/j.enbuild.2011.10.018

[22] Y. Zeng, Z. Zhang, A. Kusiak, Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms, Energy, 86 (2015), 393-402. https://doi.org/10.1016/j.energy.2015.04.045

[23] Y. J. Kim, A supervised-learning-based strategy for optimal demand response of an HVAC system in a multi-zone office building, IEEE Trans. Smart Grid, 11 (2020), 4212-4226. https://doi.org/10.1109/TSG.2020.2986539

[24] S. He, S. L. Dai, Z. Zhao, T. Zou, Y. Ma, UDE-based distributed formation control for MSVs with collision avoidance and connectivity preservation, IEEE Trans. Ind. Inf., 20 (2024), 1476-1487. https://doi.org/10.1109/TII.2023.3274234

[25] G. Lymperopoulos, P. Ioannou, Building temperature regulation in a multi-zone HVAC system using distributed adaptive control, Energy Build., 215 (2020), 109825. https://doi.org/10.1016/j.enbuild.2020.109825

[26] S. Ma, Y. Zou, S. Li, Coordinated control for air handling unit and variable air volume boxes in multi-zone HVAC system, J. Process Control, 107 (2021), 17-26. https://doi.org/10.1016/j.jprocont.2021.09.008

[27] C. Cui, J. Xue, L. Liu, Optimal control of HVAC systems through active distur- bance rejection control-assisted reinforcement learning, Energy, 323 (2025), 135824. https://doi.org/10.1016/j.energy.2025.135824

[28] J. Mei, X. Xia, Distributed control for a multi-evaporator air conditioning system, Control Eng. Pract., 90 (2019), 85-100. https://doi.org/10.1016/j.conengprac.2019.06.017

[29] G. Chen, H. Zhang, H. Hui, Y. Song, Fast Wasserstein-distance-based distributionally robust chance-constrained power dispatch for multi-zone HVAC systems, IEEE Trans. Smart Grid, 12 (2021), 4016-4028. https://doi.org/10.1109/TSG.2021.3076237

[30] Q. Jing, Y. Guo, Y. Liu, Y. Wang, C. Du, X. Liu, Optimization study of energy saving control strategy of carbon dioxide heat pump water heater system under the perspective of energy storage, Appl. Therm. Eng., 283 (2025), 129030. https://doi.org/10.1016/j.applthermaleng.2025.129030

[31] Y. Liu, C. Cui, A bi-level real-time optimal control strategy for thermal coupled multi- zone dedicated outside air system-assisted HVAC systems, Energy, 306 (2024), 132343. https://doi.org/10.1016/j.energy.2024.132343

[32] J. Mei, Z. Lu, J. Hu, Y. Fan, Energy-efficient optimal guaranteed cost intermittent-switch control of a direct expansion air conditioning system, IEEE/CAA J. Autom. Sin., 8 (2021), 1852-1866. https://doi.org/10.1109/JAS.2020.1003447

[33] H. Zhang, L. Cai, Decentralized nonlinear adaptive control of an HVAC system, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 32 (2002), 493-498. https://doi.org/10.1109/TSMCC.2002.807271

[34] A. Adegbenro, M. Short, C. Angione, An integrated approach to adaptive control and supervisory optimisation of HVAC control systems for demand response applications, Energies, 14 (2021), 2078. https://doi.org/10.3390/en14082078

[35] M. A. Abuhussain, B. S. Alotaibi, M. S. Aliero, M. Asif, M. A. Alshenaifi, Y. A. Dodo, Adaptive HVAC system based on fuzzy controller approach, Appl. Sci., 13 (2023), 11354. https://doi.org/10.3390/app132011354

[36] C. Wang, D. J. Hill, Learning from neural control, IEEE Trans. Neural Networks, 17 (2006), 130-146. https://doi.org/10.1109/TNN.2005.860843

[37] Y. Pan, H. Yu, Biomimetic hybrid feedback feedforward neural-network learn- ing control, IEEE Trans. Neural Networks Learn. Syst., 28 (2017), 1481-1487. https://doi.org/10.1109/TNNLS.2016.2527501

[38] R. Kamalapurkar, B. Reish, G. Chowdhary, W. E. Dixon, Concurrent learning for parameter estimation using dynamic state-derivative estimators, IEEE Trans. Autom. Control, 62 (2017), 3594-3601. https://doi.org/10.1109/TAC.2017.2671343

[39] H. Shi, S. He, S. L. Dai, C. Dong, Cooperative learning from neural formation control for uncertain marine surface vehicles with input saturation under switching topology, IEEE Trans. Transp. Electrif., 10 (2024), 7828-7839. https://doi.org/10.1109/TTE.2024.3400366

[40] M. Shen, X. Wang, S. Zhu, Z. Wu, T. Huang, Data-driven event-triggered adaptive dynamic programming control for nonlinear systems with input saturation, IEEE Trans. Cybern., 54 (2024), 1178-1188. https://doi.org/10.1109/TCYB.2023.3337779

[41] Y. Chen, S. Treado, Development of a simulation platform based on dynamic models for HVAC control analysis, Energy Build., 68 (2014), 376-386. https://doi.org/10.1016/j.enbuild.2013.09.016

[42] Y. Jiang, S. Zhu, Q. Xu, B. Yang, X. Guan, Hybrid modeling-based temperature and hu- midity adaptive control for a multi-zone HVAC system, Appl. Energy, 334 (2023), 120622. https://doi.org/10.1016/j.apenergy.2022.120622