Abstract
In this paper, a problem of active fault diagnosis for jump Markov nonlinear systems with non-Gaussian noises is considered. The imperfect state information formulation is transformed using sufficient statistics to a dynamical optimization problem that can be solved using approximate dynamic programming. The sufficient statistics are produced using the Bayesian recursive relations and particle filter algorithm. A special structure of approximate Bellman function is chosen to reduce a complexity caused by high dimension of statistics obtained from the particle filter. The proposed active fault detector design is compared with an extended Kalman filter based design in the simulation example.
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