Litcius/Paper detail

Finite-Horizon $H_{\infty }$ Filtering via a High-Rate Network With the FlexRay Protocol

Shuai Liu, Zidong Wang, Licheng Wang, Guoliang Wei

2022IEEE Transactions on Automatic Control38 citationsDOI

Abstract

This article addresses the finite-horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> filtering problem for a class of discrete time-varying nonlinear systems over high-rate networks whose signal exchanges are scheduled by the FlexRay protocol. To improve the efficiency of the data transmission, a high-rate network is deployed for the measurement signals to be broadcasted from the sensors to the filter. The FlexRay protocol is embedded into the high-rate network to orchestrate the transmission rule of the signals with different attributes, thereby further enhancing the transmission flexibility. On the basis of the round-robin and try-once-discard protocols, a novel FlexRay-protocol-based measurement model is proposed by using certain data holding strategies. A sufficient condition is provided to guarantee that the filtering error dynamics meets the desirable disturbance attenuation level against exogenous disturbances over a finite horizon. The parameterized form of the filter gain is attained by the solutions to some matrix inequalities. Numerical simulation is carried out to substantiate the proposed filter design algorithm.

Topics & Concepts

FlexRayParameterized complexityTransmission (telecommunications)Computer scienceFilter (signal processing)AlgorithmProtocol (science)ControllabilityCommunications protocolControl theory (sociology)Real-time computingMathematicsComputer networkEngineeringApplied mathematicsTelecommunicationsArtificial intelligenceComputer visionAerospace engineeringMedicineAutomotive industryAlternative medicinePathologyControl (management)Stability and Control of Uncertain SystemsStability and Controllability of Differential EquationsNeural Networks Stability and Synchronization