Robust weighted H∞ filtering for networked systems with intermittent measurements of multiple sensors

Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.

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H∞ILC Design for Discrete Linear Systems with Packet Dropouts and Iteration-Varying Disturbances

Discrete Dynamics in Nature and Society ◽

10.1155/2014/587323 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Bu Xuhui ◽

Zhang Hongwei ◽

Song YunZhong ◽

Yu Fashan

Keyword(s):

Stochastic System ◽

Networked Systems ◽

Disturbance Attenuation ◽

Linear Matrix ◽

Control Law ◽

Roesser Model ◽

Stochastic Variable ◽

Binary Distribution ◽

Discrete Linear Systems ◽

Intermittent Measurements

AnH∞iterative learning controller is designed for networked systems with intermittent measurements and iteration-varying disturbances. By modeling the measurement dropout as a stochastic variable satisfying the Bernoulli random binary distribution, the design can be transformed intoH∞control of a 2D stochastic system described by Roesser model. A sufficient condition for mean-square asymptotic stability andH∞disturbance attenuation performance for such 2D stochastic system is established by means of linear matrix inequality (LMI) technique, and formulas can be given for the control law design simultaneously. A numerical example is given to illustrate the effectiveness of the proposed results.

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Two equivalent multi-sensor Kalman filters with variable delays and intermittent measurements

10.36227/techrxiv.12298757.v1 ◽

2020 ◽

Author(s):

Babak Tavassoli ◽

Parisa Joshaghani

Keyword(s):

Kalman Filtering ◽

Kalman Filters ◽

Measurement Data ◽

Upper Bounds ◽

Floating Point ◽

Time Varying ◽

Missing Measurements ◽

Multiple Sensors ◽

Variable Delays ◽

Intermittent Measurements

Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.

Download Full-text