• Sliding mode variable structure (SMVS) is a nonlinear control method, which is adaptive to disturbance and parameter variations.

    滑模变结构控制是一种非线性控制方法,对系统的参数变化和扰动具有安全的自适应性。

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  • Computer simulation shows that the control of fuzzy variable structure can increase the robustness, solve the problem of shiver at high frequency, and improve the stability of small disturbance.

    计算机仿真结果表明,采用模糊变结构附加励磁控制方法可以提高鲁棒性、解决高频抖动问题,并能够很好地改善电力系统的小干扰稳定性。

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  • Good controlling results are gotten by using periodic pulses disturbance method proportioned with system variable to control the chaos.

    利用正比于系统变量的周期脉冲扰动法对其混沌进行控制,得到较好的控制结果。

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  • The paper proposes a discrete-time variable controller with a decoupled disturbance compensator.

    提出了一种带有解耦干扰补偿器的离散变结构控制方法。

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  • In process of continuous induction heat treatment, there are heavy electromagnetic disturbance, nonlinearity. heavy noise and variable parameters. The existed methods of control are difficult.

    连续感应热处理过程存在着强电磁场干扰、非线性、强噪声和过程参数多变性,传统的控制方法难以奏效。

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  • The simulation results prove that the three-phase phase-locked loop can work well in disturbance situation and variable frequency system when the frequency of the inputs is variable.

    并对相位突变和频率突变的情况进行了仿真研究,说明在相位和频率发生变动时三相锁相环仍能有效地锁定相位,能够满足系统变频的要求。

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  • Objective: To study the disturbance creatd by the variable transformation within regression parameters of nonlinear biology models and find a new method removing or reducing the disturbance.

    目的:研究变量代换对非线性生物模型回归参数的干扰,寻找消除或减少该干扰的新方法。

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  • Owing to applying the model of sliding mode variable-structure control that has compensation of disturbance monitor, switching control function was controlled by sliding mode.

    模型用带扰动观测器补偿的滑模变结构控制,对控制函数切换并受滑动模态控制。

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  • The influence of both NN approximation error and external disturbance can be attenuated to a desired level of a weight to control variable is appropriatly chosen.

    该控制方案对NN学习的近似误差以及外部干扰有强的鲁棒性。

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  • The influence of both NN approximation error and external disturbance can be attenuated to a desired level of a weight to control variable is appropriatly chosen.

    该控制方案对NN学习的近似误差以及外部干扰有强的鲁棒性。

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