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

Jun Wang (), Baocang Ding and Ping Wang
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Jun Wang: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Baocang Ding: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Ping Wang: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Energies, 2022, vol. 15, issue 21, 1-20

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.

Keywords: boiler-turbine system; subspace identification; model predictive control; terminal constraint; nominal stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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