Based on separation theory for descriptor systems, control systems can tolerate sensor failure by adjusting the parameters of observer gain matrix.
根据广义系统的分离定理，当传感器失效，利用调整观测器增益阵参数的办法来实现容错。
Sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain are derived through the numerical solution of a set of coupled linear matrix inequalities(LMI).
用矩阵不等式给出了模糊反馈增益和模糊观测器增益的存在的充分条件，并将这些条件转化为线性矩阵不等式（LMI）的可解性。
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.
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。
By selecting the gain of the observer, the observer can guarantee the estimated states converge exponentially to the true states of the system with arbitrary rate of convergence.
该降维观测器能保证估计状态以指数规律渐近趋近系统的真实状态，并且通过观测器增益参数的适当选取，可使状态估计误差以指定的收敛速度趋于零。
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。
A high gain observer is employed to obtain the estimation of states and then output feedback controller is constructed.
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。
This is because a photon takes an increasingly long time to escape from the pull of the black hole to allow the distant observer to gain information on the object's fate.
这是因为一个光子获得了长时间来逃逸黑洞引力，就使得远距离观察者获得物体命运的资讯。
The method presents the parametric expression for the gain matrix of the high-order PI observer. The contained parameters satisfy the needs of two constraints and are completely free as well.
该参数化方法给出了该类观测器增益矩阵的参数化表达式，其所含参数除了满足两个约束条件之外是完全自由的。
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.
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.
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。
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