On $H_{\infty}$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks

Cited 76|Views91

Abstract

This paper is concerned with the H filtering problem for a class of time-varying nonlinear delayed system under high-rate communication network and stochastic protocol (SP). The communication between the sensors and the state estimator is implemented via a shared high-rate communication network in which multiple transmissions are generated between two adjacent sampling instants of sensors. At each transmission instant, only one sensor is allowed to get access to the communication network in order to avoid data collisions and the SP is employed to determine which sensor obtains access to the network at a certain instant. The mapping technology is applied to characterize the randomly switching behavior of the data transmission resulting from the utilization of the SP. The aim of the problem addressed is to design an estimator such that the H disturbance attenuation level is guaranteed for the estimation error dynamics over a given finite horizon. Sufficient conditions are derived for the existence of the finite-horizon filter satisfying the prescribed H performance requirement, and the explicit expression of the time-varying filter gains is characterized by resorting toa set of recursive matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filter design scheme.

Translated text

Key words

Protocols,Communication networks,Symmetric matrices,Time-varying systems,Linear matrix inequalities,Sensor phenomena and characterization

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

On H∞ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks

On $H_{\infty}$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks