Robust Identification-Based State Derivative Estimation for Nonlinear Systems

Bhasin S.; Kamalapurkar R.; Dinh H. T.; Dixon W. E.

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Robust Identification-Based State Derivative Estimation for Nonlinear Systems

机译：基于鲁棒辨识的非线性系统状态导数估计

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A robust identification-based state derivative estimation method for uncertain nonlinear systems is developed. The identifier architecture consists of a recurrent multilayer dynamic neural network which approximates the system dynamics online, and a continuous robust feedback Robust Integral of the Sign of the Error (RISE) term which accounts for modeling errors and exogenous disturbances. Numerical simulations provide comparisons with existing robust derivative estimation methods including: a high gain observer, a 2-sliding mode robust exact differentiator, and numerical differentiation methods, such as backward difference and central difference.

机译：提出了一种基于鲁棒辨识的不确定非线性系统状态导数估计方法。标识符体系结构包括一个递归的多层动态神经网络，它可以在线近似系统动力学；以及一个连续的鲁棒反馈误差符号（RISE）项的鲁棒积分，可以对误差和外源性干扰进行建模。数值模拟与现有的鲁棒导数估计方法进行了比较，这些方法包括：高增益观测器，2滑模鲁棒精确微分器以及数字微分方法，例如后向差分和中心差分。

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来源
《IEEE Transactions on Automatic Control》 |2013年第1期|187-192|共6页
作者
Bhasin S.; Kamalapurkar R.; Dinh H. T.; Dixon W. E.;
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作者单位

Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India;

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正文语种 eng
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关键词
Convergence; Noise; Nonlinear systems; Observers; Robustness; Stability analysis; Derivative estimation; differentiator; dynamic neural network; nonlinear observer; robust identification;

机译：收敛;噪声;非线性系统观察员;坚固性稳定性分析;导数估计;微分器动态神经网络非线性观察者可靠的识别;

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Robust Identification-Based State Derivative Estimation for Nonlinear Systems

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