估计状态通过引入高增益观测器得到，实现了系统的输出反馈控制。
A high gain observer is employed to obtain the estimation of states and then output feedback controller is constructed.
用高斯径向基函数(RBF)神经网络逼近对象未知非线性，用高增益观测器估计系统不可测量状态。
Gaussian based radial basis function (RBF) neural networks are used to approximate the plant's unknown nonlinearities, and a high-gain observer is used to estimate the unmeasured states of the system.
为克服分散鲁棒控制器设计中输出量各阶导数可测要求的限制，设计了带有高增益观测器的非线性鲁棒控制器。
A nonlinear robust controller with a high gain observer (ONRC) was developed for a decentralized robust controller for use when not every derivative of the output is measurable.
然后，在系统状态不完全可测的情况下，通过设计高增益观测器对系统的状态进行估计，实现输出反馈控制器设计。
Then we design the output feedback controller by introducing the estimator of the states for the case where system states are unknown.
通过引入积分型变结构切换函数及高增益误差观测器，基于李雅普·诺夫稳定性理论，证明了闭环系统是全局稳定的，输出跟踪误差都收敛到零。
By introducing integral variable structure and high gain observer, the closed-loop control systems is shown to be globally stable in terms of Lyapunov theory, with tracking error converging to zero.
通过引入积分型变结构切换函数及高增益误差观测器，基于李雅普·诺夫稳定性理论，证明了闭环系统是全局稳定的，输出跟踪误差都收敛到零。
By introducing integral variable structure and high gain observer, the closed-loop control systems is shown to be globally stable in terms of Lyapunov theory, with tracking error converging to zero.
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