An adaptive sliding mode control technique is presented for the control of high speed feed drives.
自适应滑模控制方法可适应于控制高速进给传动。
So an adaptive feed forward control method is proposed, this method basing on robot dynamics adopts ideal trajectory on workspace to control robot, its stability is analyzed.
因此本文根据机器人动力学模型的性质设计了一种利用机器人工作空间理想轨迹控制的自适应前馈控制方法，并对自适应控制下系统的稳定性进行了分析。
Among active control techniques, adaptive feed-forward control technique is popular.
在主动控制技术中，自适应前馈控制方法是最常用的方法。
In this paper, the optimization design for self-adaptive control system of feed-forward neural network is proposed based on chaotic variable.
基于混沌变量，提出一种神经网络自适应控制系统的优化设计方案。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
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