Robust state estimation for uncertain linear discrete systems with d-step measurement delay and deterministic input signals

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2023-02-20 DOI:10.1049/csy2.12080
Yu Tian, Fanli Meng, Yao Mao, Junwei Gao, Huabo Liu
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Abstract

In this study, the state estimation problems for linear discrete systems with uncertain parameters, deterministic input signals and d-step measurement delay are investigated. A robust state estimator with a similar iterative form and comparable computational complexity to the Kalman filter is derived based on the state augmentation method and the sensitivity penalisation of the innovation process. It is discussed that the steady-state properties such as boundedness and convergence of the robust state estimator under the assumptions that the system parameters are time invariant. Numerical simulation results show that compared with the Kalman filter, the obtained state estimator is more robust to modelling errors and has nice estimation accuracy.

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具有d步测量延迟和确定性输入信号的不确定线性离散系统的鲁棒状态估计
研究了具有不确定参数、确定输入信号和d阶测量延迟的线性离散系统的状态估计问题。基于状态增强法和创新过程的灵敏度惩罚,导出了一种迭代形式与卡尔曼滤波器相似、计算复杂度与卡尔曼滤波器相当的鲁棒状态估计器。在系统参数是时不变的假设下,讨论了鲁棒状态估计器的有界性和收敛性等稳态性质。数值仿真结果表明,与卡尔曼滤波相比,所得到的状态估计器对建模误差具有更强的鲁棒性,并且具有较好的估计精度。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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