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Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model

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  • Jun Wang

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Institute of Industrial Internet, Chongqing University of Posts and Telecommunications, Chongqing 401122, China)

  • Baocang Ding

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Ping Wang

    (College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Institute of Industrial Internet, Chongqing University of Posts and Telecommunications, Chongqing 401122, China)

Abstract

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace identification method is first utilized to obtain a linear state-space model, and transformed into an input–output model. By taking the inputs and outputs of the input–output model as system states, an augmented non-minimal state-space (NMSS) model of state measurable is constructed. In order to reduce the computation burden, the augmented NMSS model is further transformed into a canonical formulation by adopting a Kalman decomposition. Based on the minimal realization state-space model, the MPC controller is parameterized as a finite-horizon optimization problem. Finally, simulations are performed and evaluated the performance of the proposed method, and the simulation results show that: the linear model approximate the non-linear system accurately; the proposed MPC method can achieve a satisfactory stable control performance; and the computation time 18.388 s for the overall optimization problem also illustrates the real-time performance effectively.

Suggested Citation

  • Jun Wang & Baocang Ding & Ping Wang, 2022. "Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model," Energies, MDPI, vol. 15(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7935-:d:953211
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    References listed on IDEAS

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    1. Hongxia Zhu & Gang Zhao & Li Sun & Kwang Y. Lee, 2019. "Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm," Sustainability, MDPI, vol. 11(18), pages 1-25, September.
    2. Strušnik, Dušan & Avsec, Jurij, 2015. "Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant," Energy, Elsevier, vol. 80(C), pages 318-330.
    3. Kong, Xiaobing & Liu, Xiangjie & Lee, Kwang Y., 2015. "Nonlinear multivariable hierarchical model predictive control for boiler-turbine system," Energy, Elsevier, vol. 93(P1), pages 309-322.
    4. Wang, Chaoyang & Liu, Ming & Zhao, Yongliang & Qiao, Yongqiang & Chong, Daotong & Yan, Junjie, 2018. "Dynamic modeling and operation optimization for the cold end system of thermal power plants during transient processes," Energy, Elsevier, vol. 145(C), pages 734-746.
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