Fault estimation for a class of linear parameter varying systems with Markovian jumps

This paper studies the problem of fault estimation for a class of linear parameter varying (LPV) Markovian jump systems (MJSs) with actuator and sensor faults. A new fault estimation scheme is presented for the LPV MJS, where a novel min-max regulator (MMR) based on the Cholesky decomposition technique is designed to adjust the parameters in the fault estimator so that the states, actuator and sensor faults are able to be estimated simultaneously with good accuracy. Then, the designed observer gains guarantee the stochastic stability of the overall error system with a prescribed H∞ performance. Finally, a numerical example is given to illustrate the effectiveness and advantages of the proposed methods.