中文摘要：

在这份报纸，一个有限时间的神经漏斗控制(FTNFC ) 计划与未知输入为马达伺服系统被建议限制。处理非光滑的输入浸透限制问题，控制的一个光滑的非仿射的函数输入信号被采用接近浸透限制，它进一步根据吝啬值的定理转变了成一种仿射的形式。一个快终端滑动模式歧管被使用构造一篇小说强迫追踪错误掉进的漏斗错误变量在内的一条 prescribe 边界一有限时间。然后，一个简单 S 字形的神经网络被利用接近包括浸透的未知系统非线性。与规定性能控制(PPC ) 不同，建议了控制避免使用的有限时间的神经漏斗逆在控制器转变了函数没有知道浸透，设计，并且能保证规定追踪的性能界限在优先。有效性和上级建议方法的性能被比较模拟结果验证。

英文摘要：

In this paper, a finite-time neural funnel control （FTNFC） scheme is proposed for motor servo systems with unknown input constraint. To deal with the non-smooth input saturation con- straint problem, a smooth non-affine function of the control input signal is employed to approximate the saturation constraint, which is further transformed into an affine form according to the mean-value theorem. A fast terminal sliding mode manifold is constructed by using a novel funnel error variable to force the tracking error falling into a prescribe boundary within a finite time. Then, a simple sigmoid neural network is utilized to approximate the unknown system nonlinearity including the saturation. Different from the prescribed performance control （PPC）, the proposed finite-time neural funnel con- trol avoids using the inverse transformed function in the controller design, and could guarantee the prescribed tracking performance without knowing the saturation bounds in prior. The effectiveness and superior performance of the proposed method are verified by comparative simulation results.