利用BP神经网络的非线性映射能力对单元机组协调控制系统被控对象进行辨识,从而建立其动态模型;在这一模型的基础上对协调控制系统中的控制器参数优化进行研究,提出基于神经网络预测控制的协调控制策略。该方法很好地解决了协调控制系统中强耦合、非线性等问题。仿真实验表明该系统的跟踪速度加快、调节精度提高、并且具有较好的抗干扰性。图6参7 Based on the non-linear mapping ability of BP neural network, the controlled object of unit coordinated control system is identified, and its dynamic model is established. Based on this model, the parameter optimization of controller in coordinated control system is studied. Coordinated Control Strategy for Network Predictive Control. The method well solves the problems of strong coupling and nonlinearity in the coordinated control system. Simulation experiments show that the tracking speed of the system is accelerated, the adjustment accuracy is improved, and the system has better anti-interference. Figure 6 Reference 7