In this paper,the fault detection filter(FDF) design problem for networked control systems(NCSs) with both network-induced delay and data dropout is studied.Based on a new NCSs model proposed recently,an observer-based filter is introduced to be the residual generator and formulated as an H∞-optimization problem for systems with two successive delay components.By applying Lyapunov-Krasovskii approach,a new sufficient condition on stability and H∞ performance is derived for systems with two successive delay components in the state.A solution of the optimization problem is then presented in terms of linear matrix inequality(LMI) formulation,dependently of time delay.In order to detect the fault,the residual evaluation problem is also considered.An illustrative design example is employed to demonstrate the validity of the proposed approach.
A novel fault detection and identification(FDI)scheme for HVDC(High Voltage Direct Current Transmission)system was presented.It was based on the unique active disturbance rejection concept,where the HVDC system faults were estimated using an extended states observer(ESO).Firstly,the mathematical model of HVDC system was constructed,where the system states and disturbance were treated as an extended state.An augment HVDC system was established by using the extended state in rectify side and converter side,respectively.Then,a fault diagnosis filter was established to diagnose the HVDC system faults via the ESO theory.The evolution of the extended state in the augment HVDC system can reflect the actual system faults and disturbances,which can be used for the fault diagnosis purpose.A novel feature of this approach is that it can simultaneously detect and identify the shape and magnitude of the HVDC faults and disturbance.Finally,different kinds of HVDC faults were simulated to illustrate the feasibility and effectiveness of the proposed ESO based FDI approach.Compared with the neural network based or support vector machine based FDI approach,the ESO based FDI scheme can reduce the fault detection time dramatically and track the actual system fault accurately.What's more important,it needs not do complex online calculations and the training of neural network so that it can be applied into practice.
