An Adaptive Takagi–Sugeno Fuzzy Model-Based Generalized Predictive Controller for Pumped-Storage Unit

In order to improve the control performance and suppress the “S” characteristics area instability of the pumped-storage unit (PSU), this paper proposes an adaptive Takagi-Sugeno fuzzy model-based generalized predictive controller (ATS-GPC) for the PSU. First, the T-S fuzzy model is used to obtain the controlled autoregressive integrated moving average (CARIMA) model, in which the fuzzy C-means (FCM) clustering algorithm is used for the identification of antecedent parameters and the least square method (LSM) is used to obtain the consequent parameters. Meanwhile, the T-S fuzzy model can be online adjusted according to the real-time tracking error feedback to decrease the influence of the initial offline trained fuzzy model. Then, the generalized predictive controller is designed for the PSU based on the CARIMA. Finally, some numerical simulation experiments including the start-up process, frequency disturbance process, frequency tracking experiments, and robustness analyses have been conducted to verify the proposed method. The experiments results have shown that the proposed ATS-GPC can significantly improve the control performance of the PSU and effectively suppress the unstable operation in “S” characteristics area. In addition, the strong robustness of the proposed controller is verified.