Abstract

Based on the state observer theory of dynamic plants operating under uncertainty, we propose a method for reconstructing high-order derivatives of an online signal (for example, a reference action in a tracking system). The method requires neither numerical differentiation nor the presence of an analytical description of the signal. The dynamic differentiator is constructed as a replica of the virtual canonical model with an unknown but bounded input. The use of bounded correction actions and a special structure of the differentiator permit one to reduce the outliers of the resulting estimates at the beginning of a transient compared with a linear differentiator with high-gain coefficients. By way of application, we consider the problem of tracking a spatial trajectory by the center of mass of an unmanned aerial vehicle and present simulation results.

中文翻译：

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具有分段线性校正信号的微分器的级联合成

摘要

基于在不确定性下运行的动态植物的状态观察器理论，我们提出了一种重建在线信号（例如，跟踪系统中的参考动作）的高阶导数的方法。该方法既不需要数值微分，也不需要信号的分析描述。动态微分器被构造为具有未知但有界输入的虚拟规范模型的副本。与具有高增益系数的线性微分器相比，使用有界校正动作和微分器的特殊结构可以减少瞬态开始时产生的估计值的异常值。通过申请，