高阶积分链微分器及其在加速度反馈中的应用
High-order integral-chain differentiator and application to acceleration feedback
AI总结：
本文通过坐标变换证明积分链微分器与高增益微分器的等价性，设计非线性积分链微分器以抑制噪声，并应用于二阶不确定系统的加速度估计。
摘要：
在适当的坐标变换下，给出了积分链微分器与通常的高增益微分器之间的等价性。积分链微分器比通常的高增益线性微分器能更彻底地抑制噪声。在积分链微分器中，扰动仅存在于最后一个微分方程中，并可通过每一层积分器得到抑制。此外，设计了一种非线性积分链微分器，它是线性积分链微分器的扩展。最后，将一个三阶微分器应用于二阶不确定系统的加速度估计。
英文摘要：
The equivalence between integral-chain differentiator and usual high-gain differentiator is given under suitable coordinate transformation. Integral-chain differentiator can restrain noises more thoroughly than usual high-gain linear differentiator. In integral-chain differentiator, disturbances only exist in the last differential equation and can be restrained through each layer of integrator. Moreover, a nonlinear integral-chain differentiator is designed which is the expansion of linear integral-chain differentiator. Finally, a 3-order differentiator is applied to the estimation of acceleration for a second-order uncertain system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
