Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

Hongxia Zhu, Gang Zhao, Li Sun and Kwang Y. Lee
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Hongxia Zhu: School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Gang Zhao: School of Energy and Environment Engineering, Southeast University, Nanjing 210096, China
Li Sun: School of Energy and Environment Engineering, Southeast University, Nanjing 210096, China
Kwang Y. Lee: Department of Electrical & Computer Engineering, Baylor University, Waco, TX 76798, USA

Sustainability, 2019, vol. 11, issue 18, 1-25

Abstract: This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

Keywords: model predictive control (MPC); local model network (LMN); immune genetic algorithm (IGA); boiler–turbine unit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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