A nonlinear direct adaptive controller based on online neural network and feedback linearization is designed by precondition of all state feedback.
在全状态反馈的前提下，设计了一种基于在线神经网络和反馈线性化的非线性直接自适应控制器。
Differential control introduced by all state feedback control could forecast the trend of system response, and effectively restrain speed overshoot.
全状态反馈控制所引入的微分控制，可以预见系统响应趋势，它的引入可以有效地抑制速度响应超调。
The key to realizing thecontrol of chaotic systems is that all the states can be measured as feedback state variables.
采用输入-状态线性化方法实现非线性乃至混沌系统控制的关键是要求系统的全部状态变量都能测量并用于反馈。
Realizing state feedback needs to measure all the state variables, which is difficult in general.
实现状态反馈需要测量所有的状态变量，这在一般情况下是困难的。
By the state feedback exact linearization method and observer theory, a control approach is presented for a class of chaotic systems in which state variables cannot be all measured.
基于状态反馈精确线性化方法并结合状态观测器设计理论，给出了一类状态不能全部测量的混沌系统的控制方案。
The results in this dissertation are all presented via state feedback and output feedback.
所得结果从反馈形式上看均采用状态反馈和输出反馈两种方式。
The results in this dissertation are all presented via state feedback and output feedback.
所得结果从反馈形式上看均采用状态反馈和输出反馈两种方式。
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