中文摘要：

伺服马达由于刺激输入电压，负担转矩和环境操作条件的效果拥有一个强烈非线性的性质。导出能够表示它的动力学和不变的特征的一个传统的数学模型因此是相当困难的。神经基于网络的适应控制策略在这篇论文被建议。在这个方法，二个神经网络分别地为系统鉴定(NNI ) 和控制(NNC ) 被采用了。然后，使用得通常学习专业化被修改了，由在重量期间作为伺服马达的近似产量拿 NNI 产量训练得到敏感信息。而且，选择学习的率的规则根据 Lyapunov 稳定性的分析被给。最后，在一台伺服马达上使用建议控制策略的一个例子被举显示出它的有效性。

英文摘要：

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.