A Reduced-Order Fault Detection Filtering Approach for Continuous-Time Markovian Jump Systems with Polytopic Uncertainties

Abstract

The fault detection (FD) reduced-order filtering problem is investigated for a family of continuous-time Markovian jump linear systems (MJLSs) with polytopic uncertain transition rates, which also include the totally known and partly unknown transition rates. Then, in accordance with the convexification techniques, a novel sufficient condition for the existence of FD reduced-order filter over MJLSs with deficient transition information is obtained in terms of linear matrix inequality (LMI), which can ensure the error augmented system with the FD reduced-order filter is randomly stable. In addition, a performance index is given to enhance the robustness of the residual system against deficient transition information and external disturbance, such that the error between the fault and the residual is made as small as possible to reinforce the faults sensitivity. Finally, the effectiveness of the proposed method is substantiated with two illustrative examples.

Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

“…Due to the increasing demand for reliability and safety and acceptable performance of control engineering systems, faulttolerant control [1][2][3][4] and fault detection [5,6] have become an attractive theory and application topic in the last decades. Fault estimation [7][8][9][10][11] is supplementary to give the exact information of faults, thereby helping to reconstruct fault signals.…”

Finite-Time Passivity and Passification Design for Markovian Jumping Systems with Mode-Dependent Time-Varying Delays

“…Another practical example of VTOL (vertical take-off and landing) helicopter is provided. The system dynamics can be modeled by[23,24] the state vector ( ) = [ 1 ( ), 2 ( ), 3 ( ), 4 ( )] ∈ R 4 , 1 ( ) denotes the horizontal velocity, 2 ( ) denotes the vertical velocity, 3 ( ) denotes the pitch rate, and 4 ( ) denotes the pitch angle, ( ) represents the control input, Time history of ( ) ( ). Time history of 2 ∫ represents the external disturbance and the system matrices are given as…”