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中文摘要：
针对难以建立较准确数学模型的非线性被控对象,提出了一种基于神经网络的数据驱动控制器参数整定法.其设计思想是结合虚拟目标值和神经网络,跳过被控对象,直接得到控制器.此外,利用李亚普诺夫理论证明了神经网络的学习速率在一定范围内可以保证控制器的跟踪误差收敛,并且利用虚拟参考反馈整定（VRFT）算法中的滤波器,结合泰勒展开式,进一步验证了闭环控制系统的稳定性.仿真表明,该方法具有计算负担小,采用数据量少,调节参数方便,强跟踪性等优点.
英文摘要：
A new method of data-driven control parameter setting based on neural network is proposed for the nonlinear controlled objects whose accurate mathematical models are diffcult to be established.The design idea is to circumvent the controlled objects and get the controller directly by combining virtual reference and neural network.In addition,the Lyapunov theory is applied to proving that neural network learning rate can guarantee the convergence of tracking error within a certain range.Then the filter of virtual reference feedback tuning（VRFT） algorithm and Taylor expansion are used to further verify the stability of the closed-loop control system.Simulation shows that the method has some advantages of a reduced computational burden,the least amount of calculation,easy parameter adjustment,and strong tracking performance and so on.
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