Distributed Non-Fragile State Estimation for Uncertain Nonlinear Systems of Sensor Networks Subject to Sensor Nonlinearities
Shihui Tian, Ke Xu, Fengshan Huang
TL;DR
This paper presents a method for robust state estimation in sensor networks with uncertain nonlinear systems and sensor nonlinearities.
Contribution
A novel distributed non-fragile state estimation approach for uncertain nonlinear systems with sensor nonlinearities and gain fluctuations.
Findings
A non-fragile control framework is developed for distributed state estimation in sensor networks.
Lyapunov–Krasovskii approach ensures passivity performance of the estimation error system.
Two examples demonstrate the effectiveness of the proposed method.
Abstract
This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communication and computation resources. In particular, the sensor nonlinearities in the sensor network and state estimation gain fluctuations are taken into account for more general applicability. With the help of the Lyapunov–Krasovskii approach, sufficient convex optimization criteria can be given so that the passivity performance of its resultant state estimation error system can be guaranteed. The optimized non-fragile state estimation gains can be further determined on the basis of solving the convex optimization. The advantages and usefulness of our developed results are finally demonstrated by…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Fault Detection and Control Systems · Stability and Control of Uncertain Systems