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Wind speed uncertainty and measurement noise affect the control effect in hydraulic wind turbine systems. This paper proposes a model predictive control (MPC) method with a dynamic Kalman filter (KF) based on a linear parameter-varying (LPV) model to address this problem. First of all, the LPV model for a nonlinear system of a hydraulic wind turbine is established using function substitution. Then, a LPV-based KF is introduced into the MPC to provide more precise estimated results and improve the anti-interference ability of the system. According to the current condition of the hydraulic wind turbine, the method updates the Kalman state estimator at each sampling instant and computes the optimal control input by solving a quadratic programming (QP) optimization problem. The performance and the efficiency of the proposed method is validated in simulation and compared with other methods.