Estimation with unknown inputs and uncertainties for sampled-data systems based on quasi sliding mode
Estimation with unknown inputs and uncertainties for sampled-data systems based on quasi sliding mode
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DOI：
10.1080/00207179.2020.1750706
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发表时间：
2020-04
影响因子：
2.1
通讯作者：
Thang Nguyen;C. Edwards;G. Herrmann
中科院分区：
文献类型：
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作者：
Thang Nguyen;C. Edwards;G. Herrmann
在这项工作中，我们考虑的问题，同时估计系统的状态和未知输入的线性采样数据系统，其动态的影响，外部干扰和不确定性。硬件的限制，防止估计方案的采样数据系统实现有限时间收敛，这是一个典型的属性，现有的动态连续时间系统的滑模观测器，因为采样周期是有限的。由于采样过程，这样的观察器，设计用于连续时间系统的近似实现，可能不会保留所需的性能在采样数据的上下文中。在本文中，我们提出了一个观察员，它利用准滑动运动的概念，同时估计的状态变量和未知的输入信号在采样数据的情况下。理论研究进行正式证明的观测器的收敛性能，同时提供仿真结果，以显示所提出的计划的效率。
In this work, we consider the problem of simultaneously estimating the system states and unknown inputs in a linear sampled-data system, whose dynamics is influenced by external disturbances and uncertainties. Hardware limitations prevent an estimation scheme for a sampled-data system from achieving finite-time convergence, which is a typical property of existing sliding mode observers for dynamical continuous-time systems, because the sampling period is finite. Due to the sampling process, an approximate implementation of such an observer, designed for a continuous-time system, may not retain the desired performance in the sampled-data context. In this paper, we present an observer which takes advantage of the quasi-sliding motion concept to simultaneously estimate the state variables and the unknown input signals in a sampled-data context. A theoretical study is conducted to formally justify the convergence properties of the observer whilst simulation results are provided to show the efficiency of the proposed scheme.