In this article, a novel unknown input interval observer (UIIO) is proposed for systems not satisfying the relative degree condition. In interval estimation, estimation errors are required not only positive but also convergent. For this purpose, some works combined unknown input observer with interval estimation, namely UIIO where by virtue of some transformations, some states are expressed as functions of outputs and other states which are decoupled from the unknown input. Nevertheless, complete decoupling can be guaranteed when the systems satisfy relative degree condition, which does not hold for some practical systems. Although some works have been done to relax the requirement on relative degree condition, they usually construct auxiliary outputs using high‐order differentiators which necessitates that the signals are smooth enough as well as that their derivatives are bounded, limiting the application of existing approaches and also increasing the computational cost. Motivated by the discussions above, a novel UIIO scheme combining the interval observation technique with adaptive sliding mode is proposed, without the relative degree condition and free from differentiating signals, hence the design process is simplified and the computational cost is decreased. Finally, simulations are conducted to verify the effectiveness of the proposed scheme.

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一种不满足相对学位条件的系统的新型未知输入区间观测器

在本文中，针对不满足相对程度条件的系统，提出了一种新颖的未知输入间隔观察器（UIIO）。在区间估计中，估计误差不仅需要正值，而且还需要收敛。为此，一些工作将未知输入观察者与间隔估计相结合，即UIIO，UIIO通过一些变换将某些状态表示为输出的函数，而将其他状态与未知输入解耦。然而，当系统满足相对程度条件时，可以保证完全解耦，这在某些实际系统中是不成立的。尽管已经做了一些工作来放宽对相对学位条件的要求，他们通常使用高阶微分器构造辅助输出，这需要信号足够平滑以及其导数有界，从而限制了现有方法的应用并增加了计算成本。基于以上讨论，提出了一种将区间观测技术与自适应滑模相结合的UIIO方案，该方案没有相对程度条件，并且没有差分信号，从而简化了设计过程，降低了计算量。最后，进行仿真以验证所提出方案的有效性。提出了一种将区间观测技术与自适应滑模相结合的UIIO方案，该方案没有相对程度条件，并且没有差分信号，简化了设计过程，降低了计算量。最后，进行仿真以验证所提出方案的有效性。提出了一种将区间观测技术与自适应滑模相结合的UIIO方案，该方案没有相对程度条件，并且没有差分信号，简化了设计过程，降低了计算量。最后，进行仿真以验证所提出方案的有效性。