Integrated Design of Input and Observer Gain for Active Fault Diagnosis Based on Hybrid Stochastic-Deterministic Approach

This paper proposes a hybrid stochastic-deterministic approach to realize the observer-based active fault diagnosis (AFD) for linear time varying systems. A novel conception called hybrid zonotope-Gaussian dispersity for random variables of systems is introduced to establish an online optimization strategy for AFD. The optimal gain of observer and the optimal input are simultaneously designed at each step by solving a non-convex quadratic fractional programming problem, which is proved to be equivalent to a mixed integer quadratic programming problem. The proposed approach avoids the strict set-separation constraint conditions of traditional offline AFD technology by making full use of the online measured outputs such that the diagnosisability of systems has potential to be further improved. At the end, a quadrotor model is used to verify the effectiveness of our proposed method.