针对混流式水轮发电机组(FTGS)复杂的非线性特性，提出了包含混流式水轮机神经网络模型(FTNNM)的FTGS的神经网络模型预测控制(NNMPC)。利用神经网络辨识模型(NNIM)预测FTGS对控制信号的反应，并采用优化算法计算来优化未来FTGS性能的控制信号。仿真结果表明NNMPC对FTGS是一个有效的工具。

This paper presents the neural network model prediction control (NNMPC) for the Francis hydroturbine generator set （FTGS） possessing nonlinear characteristics. The neural network identification model (NNIM) is used to predict future response to potential control signals of the FTGS. An optimization algorithm can compute the control signals that optimize future FTGS performance. Simulated results show that NNMPC is an effective tool for the nonlinear FTGS.