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Nonlinear dynamic investigation and anti-bifurcation control of a boiler-turbine unit via dual-mode fuzzy model predictive control strategy

The increase in renewable energy sources connected to the grid has increased flexibility requirements in the operation of thermal power plants. Because of severe nonlinearity and various disturbances, the dynamic behavior of the boiler-turbine unit will change significantly at different operating co...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2021-10, Vol.358 (15), p.7365-7393
Main Author: Zhu, Jianzhong
Format: Article
Language:English
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Summary:The increase in renewable energy sources connected to the grid has increased flexibility requirements in the operation of thermal power plants. Because of severe nonlinearity and various disturbances, the dynamic behavior of the boiler-turbine unit will change significantly at different operating conditions. Further, modeling uncertainties cofound definitive knowledge about the changes, exacerbating efforts to control. In this paper, a general disturbed model of the boiler-turbine unit for control design is determined through the nonlinear dynamic analysis on the concepts of bifurcation and limit cycles behavior rather than given by an artificial chosen. Then, a dual-mode fuzzy predictive control strategy is proposed on discrete-time Takagi-Sugeno fuzzy model. Online optimal problem of the approach is solved to accomplish the fast-tracking task; to achieve the anti-bifurcation control objective, a novel minimal robust positively invariant set corresponding with a local controller is offline obtained in order to change the undesired nonlinear behavior into stable limit cycles. With the input-to-state stability theory, the proposed control strategy is theoretically proved and given in terms of linear matrix inequalities. Simulation carried out on the boiler-turbine unit demonstrates that the proposed dual-mode control strategy achieves the disturbance suppression ability and the improvement of the nonlinear dynamic behavior.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2021.07.029