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针对一类强耦合并且带非线性不可测干扰的线性系统,提出了一种基于神经网络干扰观测器的近似最优解耦控制方法.首先,采用递归神经网络对干扰进行观测;然后设计前馈解耦控制器,将强耦合系统转化为多个单变量系统;最后,基于神经网络干扰观测器,设计近似最优跟踪控制器.以球磨机为仿真对象,数值仿真实验验证了该方法可以消除回路间的耦合和不可测干扰的影响,达到了理想的控制效果.“,”We propose an approximate optimal decoupling control method based on a neural network disturbance observer for a class of linear systems with strong couplings and nonlinear immeasurable disturbances. First, we use a recurrent neural network to observe the disturbance. Then, we design a feed-forward decoupling controller to convert the strong coupling system into multiple single-variable systems. Finally, based on the neural network disturbance observer, we design an approximate optimal tracking controller. Numerical simulation experiments, using a ball mill as a simulation object, verify that this method can eliminate the effects of coupling and immeasurable interference between loops and achieve an ideal control effect.