Why dynamic high type control method can improve tracking precision is analyzed in theory and effects on dynamic performances of the method are analyzed, too. A mathematic simulation is carried out.
从理论上分析了动态高型控制方法之所以能够提高跟踪精度的原因及其对系统动态性能的影响，并对该方法进行了仿真研究。
We promote the precision of control system by using Kalman filter to process the sample data and intensify the dynamic tracking properties by adding feed forward.
对采样数据进行卡尔曼滤波以提高控制的精度并且加入前馈增强系统的动态跟踪性能。
Using dynamic recurrent neural networks as identification and controller, the minimum error control of robot tracking the idea locus is implemented.
采用动态对角回归神经网络作为辨识器和控制器，实现了机器人轨迹跟踪的最小误差控制。
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