Fuzzy Model Predictive Tracking Control for Boiler-Turbine Systems with Disturbances and Input Constraints

Lei Kong, Rongrong Gu, Quan Wang (), Xiaofei Ju and Wenjing Ding
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Lei Kong: School of IoT Engineering, Wuxi University, Wuxi 214105, China
Rongrong Gu: School of IoT Engineering, Wuxi University, Wuxi 214105, China
Quan Wang: School of IoT Engineering, Wuxi University, Wuxi 214105, China
Xiaofei Ju: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Wenjing Ding: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

Mathematics, 2025, vol. 13, issue 23, 1-20

Abstract: During the actual operation of a boiler-turbine system (BTS), it inevitably encounters various unknown disturbances and model uncertainty. Additionally, due to physical limitations, there are constraints on both the magnitude and the change rate of unit controls. These factors generally cause instability of the BTS and prevent it from achieving zero-offset tracking of load commands. To address this issue, this paper proposes a fuzzy model predictive tracking control (FMPTC) scheme for the nonlinear BTS with disturbances and input constraints. With the proposed FMPTC scheme, the closed-loop system is theoretically proved to be asymptotically stable and offset-free in output tracking; meanwhile, the constraints on the input magnitude and change rate are satisfied by both the free control variable and the future control input in the form of the state feedback law. The simulation results for a 300 MW unit demonstrate the advantages of the proposed control scheme.

Keywords: fuzzy model predictive control; offset-free tracking; input constraint; disturbance rejection; boiler-turbine system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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