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工业运行过程的动态模型由底层设备层被控对象的动态模型和上层运行层生产过程的动态模型两部分组成.针对一类强耦合工业运行过程,提出了基于数据采样的最优解耦控制方法.该方法在底层设备层,将基于数据采样的控制问题转化为时变时滞系统的稳定性问题,基于Lyapunov-Krasovskii函数,给出了基于数据采样的状态反馈控制器参数.由于底层设备层采样使得上层运行层的动态模型为既有连续信号又有离散信号的混杂模型,因此在上层运行层,首先将混杂模型离散化,然后针对离散化后的运行过程广义模型,采用解耦控制与最优跟踪控制相结合的方法设计最优解耦控制器.通过数值例子的仿真对比实验说明了所提方法的有效性.“,”The dynamic model of the industrial operation process is composed of the dynamic model of the underlying device layer controlled object and the upper operating layer production process.We propose an optimal decoupling control method based on data sampling for a class of strong coupling industrial processes.The method transforms the control problem based on data sampling into the stability problem of time-varying delay system in the underlying device layer.On the basis of the Krasovskii-Lyapunov function, we present the parameters of the state feedback controller.The dynamic model of the industrial operation process is a hybrid model that includes continuous and discrete signals because of the sampling of the underlying device layer.Thus, in the upper operating layer, the hybrid model is first discretized.Then, for a discrete generalized model of operational processes, we design the optimal decoupling controller by combining the decoupling control and the optimal tracking control method.Finally, a numerical simulation experiment is performed to demonstrate the effectiveness of the proposed method.