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A new set of multivariable predictive control algorithms for time-delayed nonsquare systems of different domains: A minimum-energy examination

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  • Hunek, Wojciech P.
  • Feliks, Tomasz

Abstract

A new approach to the minimum-energy design of stochastic inverse model control-oriented predictive control algorithms dedicated to the multivariable physical systems is proposed in the paper. For this reason, the novel transfer-function-type stochastic solutions in the forms of respective continuous-time minimum variance control (CMVC) and discrete-time minimum variance control (DMVC), both employing generalized inverses, are examined. The theoretical and practical simulation examples confirm high advantages of the original σ and Smith factorization-oriented inverses over the benefits derived from the well-established Moore–Penrose inverse regarding the energy-based robustification of the discussed control procedures. Henceforth, from now on, the industrial real-life systems can be developed toward a minimum-energy consumption at the same time preserving the maximum-speed and maximum-accuracy important maintenance for modern sustainable energy plants.

Suggested Citation

  • Hunek, Wojciech P. & Feliks, Tomasz, 2025. "A new set of multivariable predictive control algorithms for time-delayed nonsquare systems of different domains: A minimum-energy examination," Applied Energy, Elsevier, vol. 381(C).
    • Handle: RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024772
    DOI: 10.1016/j.apenergy.2024.125093
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    1. Sun, Guoxin & Yu, Yongheng & Yu, Qihui & Tan, Xin & Wu, Linfeng & Wang, Yahui, 2024. "Enhancing control and performance evaluation of composite heating systems through modal analysis and model predictive control: Design and comprehensive analysis," Applied Energy, Elsevier, vol. 357(C).
    2. Xie, Jiahang & Yang, Rufan & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2023. "PID-based CNN-LSTM for accuracy-boosted virtual sensor in battery thermal management system," Applied Energy, Elsevier, vol. 331(C).
    3. Brown, Sarah & Beausoleil-Morrison, Ian, 2023. "Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada," Applied Energy, Elsevier, vol. 349(C).
    4. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    5. Zhu, Qun-Xiong & Zhang, Chen & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry," Applied Energy, Elsevier, vol. 213(C), pages 322-333.
    6. Thilker, Christian Ankerstjerne & Madsen, Henrik & Jørgensen, John Bagterp, 2021. "Advanced forecasting and disturbance modelling for model predictive control of smart energy systems," Applied Energy, Elsevier, vol. 292(C).
    7. Matino, Ismael & Dettori, Stefano & Colla, Valentina & Weber, Valentine & Salame, Sahar, 2019. "Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
    9. Hosius, Emil & Seebaß, Johann V. & Wacker, Benjamin & Schlüter, Jan Chr., 2023. "The impact of offshore wind energy on Northern European wholesale electricity prices," Applied Energy, Elsevier, vol. 341(C).
    10. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    11. Vidrih, Boris & Arkar, Ciril & Medved, Sašo, 2016. "Generalized model-based predictive weather control for the control of free cooling by enhanced night-time ventilation," Applied Energy, Elsevier, vol. 168(C), pages 482-492.
    12. Chen, Ruihu & Yang, Chao & Ma, Yue & Wang, Weida & Wang, Muyao & Du, Xuelong, 2022. "Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set," Applied Energy, Elsevier, vol. 323(C).
    13. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    14. Zhang, Jianhua & Zhou, Yeli & Li, Ying & Hou, Guolian & Fang, Fang, 2013. "Generalized predictive control applied in waste heat recovery power plants," Applied Energy, Elsevier, vol. 102(C), pages 320-326.
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