A Markov chain plays an important role in an interactingmultiplemodel (IMM) algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and...
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A Markov chain plays an important role in an interactingmultiplemodel (IMM) algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and discrete modes. The switching between system modes is governed by a Markov chain. In real world applications, this Markov chain may change or needs to be changed. Therefore, one may be concerned about a target tracking algorithm with the switching of a Markov chain. This paper concentrates on fault-tolerant algorithm design and algorithm analysis of IMM estimation with the switching of a Markov chain. Monte Carlo simulations are carried out and several conclusions are given.
An observer-based fault detection scheme utilizing the interactingmultiplemodel ( IMM ) approach is presented for an industrial actuator benchmark problem. The requirement is to detect current and position faults in...
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An observer-based fault detection scheme utilizing the interactingmultiplemodel ( IMM ) approach is presented for an industrial actuator benchmark problem. The requirement is to detect current and position faults in a brushless D.C. motor which is subject to both actuator and sensor faults in the presence of unknown load torques. Fault detection is carried out by threshold checking on the model probabilties calculated by the algorithm. The proposed scheme has been applied to the given benchmark data with current and position fault detection achieved in a good detection time and with a high detection probabilty, regardless of the load torque.
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