Neural network adaptive control is proposed for a class of nonlinear system preceded by hysteresis. A novel model is developed to represent the hysteresis characteristics in explicit form. Furthermore, the auxiliary variable of the proposed model is proved to be bounded, which is essential for controller design. Then, neural network adaptive controller is directly applied to mitigate the influence of the hysteresis without constructing the hysteresis inverse. The updated law and control law of the controllers are derived from Lyapunov stability theorem, so that the boundedness of the close-loop system is guaranteed. Finally, the experimental tests are carried out to validate the effectiveness of the proposed approach.

中文翻译：

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滞后前非线性系统的神经网络自适应控制

针对一类具有滞后现象的非线性系统，提出了神经网络自适应控制。开发了一种新模型来以显式形式表示滞后特性。此外，证明了所提出模型的辅助变量是有界的，这对于控制器设计至关重要。然后，直接应用神经网络自适应控制器来减轻滞后的影响，而无需构造滞后逆。根据李雅普诺夫稳定性定理推导出控制器的更新律和控制律，从而保证闭环系统的有界性。最后，进行了实验测试以验证所提出方法的有效性。