模型预测控制（MPC）已越来越多地应用于城市排水系统中闸泵的协同调控，以最大化利用现有调蓄能力以缓解内涝风险。然而，利用观测数据提升模型预测性能的研究仍然有限。在此背景下，如何对城市排水系统状态进行精准预测，得到可靠的排水系统控制策略以减轻极端降雨事件的负面影响显得至关重要。提出一种新型模型预测控制框架，即无迹卡尔曼滤波的模型预测控制（UKF-MPC）方法，该框架耦合了无迹卡尔曼滤波（UKF）和模型预测控制（MPC）方法，通过动态误差校正机制提升预测的整体精度，并在福州市斗门区域进行应用研究。结果表明，UKF-MPC通过动态融合观测信息与模型预测输出，相对性能指数（RIP）的均值达36.4%。与传统MPC方法相比，UKF-MPC所得控制策略在不确定环境下具有更好的适用性与稳健性。

[Objective] Model Predictive Control （MPC） has been increasingly applied to the coordinated regulation of sluices and pumps in urban drainage systems to maximize the utilization of existing storage capacity and mitigate waterlogging risks. However，research on leveraging observational data to improve model prediction performance remains limited. This study focuses on measurement uncertainty in real-time control of urban drainage systems and proposes an Unscented Kalman Filter-based Model Predictive Control （UKF-MPC） method to enhance system adaptability under complex conditions. [Methods] By integrating model predictions with sensor observations，the proposed method incorporates data assimilation into the error feedback strategy and state update of the MPC prediction model，establishing a real-time data-driven MPC framework. The approach was validated in the Doumen area of Fuzhou City. [Results] （1） UKF-MPC significantly improved flow prediction accuracy by dynamically fusing observational information with model outputs，increasing the Nash-Sutcliffe Efficiency （NSE） from 0.39-0.58 to 0.56-0.79，with a mean Relative Improvement Percentage （RIP） of 36.4%. （2） Through data assimilation，the method upgraded traditional error feedback to state-space correction. Under 50-year and 100-year return period storm scenarios，UKF-MPC consistently generated more robust control strategies，enhancing system adaptability in complex environments and providing a framework capable of real-time system state perception and dynamic control optimization. [Conclusions] Compared with conventional MPC，the control strategies derived from UKF-MPC exhibit superior applicability and robustness under uncertain conditions. Future research will quantitatively analyze the influence of UKF parameter settings on correction performance and conduct systematic validation across multiple watersheds and diverse rainfall events using richer observational data，to further clarify the applicability boundaries and parameter sensitivity mechanisms of UKF-MPC. On this basis，further exploration will focus on incorporating state covariance information directly into the optimization problem to develop a risk-aware model predictive control method.