Distributed UIO for Multiple Sensor Systems With Unknown Inputs and Measurement Noise

IF 3.8 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Fanglai Zhu, Mingwei Li, Tianbiao Shi, Xufeng Ling
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引用次数: 0

Abstract

In the article, a distributed unknown input observer (DUIO) constructing technique for a system with unknown input (UI) and measurement noise (MN) is come up with. Specially, multiple sensor groups are equipped to measure the output of the target system, and the output information measured by each sensor group is not enough for estimating the system states individually. It is assumed that each sensor group suffers from MN. To begin with, an output transformation and a state shift transformation are made such that the transformed subsystem contains neither UI nor MN. Next, for each transformed subsystem, a distributed state observer is designed to obtain the asymptotic convergent state estimation, and also the MN reconstruction (MNR). Furthermore, an output interval estimation method is proposed, and a correlation of the UI and the state is come up with algebraically. Then, by using the relationship, an asymptotic convergent UI reconstruction (UIR) method is developed. Finally, a simulation example is given to verify the effectiveness of the proposed method.

具有未知输入和测量噪声的多传感器系统的分布式UIO
针对具有未知输入和测量噪声的系统,提出了一种分布式未知输入观测器(DUIO)构建技术。具体来说,采用多传感器组来测量目标系统的输出,每个传感器组测量的输出信息不足以单独估计系统的状态。假设每个传感器组都有MN。首先,进行输出转换和状态转换,使转换后的子系统既不包含UI也不包含MN。然后,对每个变换后的子系统设计分布式状态观测器,得到渐近收敛的状态估计,并得到MN重构(MNR)。在此基础上,提出了一种输出区间估计方法,并给出了用户界面与状态的代数关联。然后,利用这一关系,提出了一种渐近收敛的用户界面重构方法。最后,通过仿真算例验证了所提方法的有效性。
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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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