Quantized Fault Detection Filter Design for Networked Control System with Markov Jump Parameters

In this paper, the design problem of fault detection filter for a class of networked control system subject to Markov jump parameters, randomly occurring uncertainties, sensor faults and packet dropouts by using state feedback control is investigated. The fault detection filter is used as an optimal residual generator that generates the residual signal and also guarantees the sensitivity of residual signal to the faults. To be more specific, a suitable threshold is selected for the residual evaluation function to reduce the error between the sensor faults and the residual signal. Further, by employing a fault detection technique along with output quantization approach, a new set of sufficient conditions is derived to ensure the stochastic stabilization of the addressed system with sensor faults under a predefined H-infinity performance level. Particularly, by constructing Lyapunov-Krasovskii functional and utilizing extended Wirtinger-based single integral inequality, the required sufficient conditions are derived in terms of linear matrix inequalities for getting the required result. At last, two numerical examples including high alpha research vehicle model are presented to demonstrate the usefulness of our developed results.

Automated summary

This paper investigates the design problem of a fault detection filter for a networked control system, considering Markov jump parameters, randomly occurring uncertainties, sensor faults and packet dropouts. By using state feedback control, the residual signal is generated with sensitivity to faults, and suitable thresholds are selected to reduce errors between sensor faults and the residual signal. Sufficient conditions are derived to ensure system stabilization with sensor faults under a predefined H-infinity performance level, utilizing a combination of fault detection techniques, output quantization approach, Lyapunov-Krasovskii functional construction, and extended Wirtinger-based single integral inequality. Two numerical examples, including a high alpha research vehicle model, are presented to demonstrate the effectiveness of the proposed approach.