This paper proposes a new type of robust fault detection and isolation filter for dynamics of HST based on descriptor systems with uncertainties in finite frequency. This filter is designed based on the unknown input filter to decouple the non-linear variables due to the aerodynamic drag pressing on the trains. The exogenous disturbance is partitioned into two parts-the decoupling one is regarded as the augmented variables of the non-linear part of the systems, and the non-decoupling one is seen as the augmented disturbance along with the uncertainties. Concurrent faults of different positions are considered, residual evaluation functions and adaptive threshold are given to judge if the faults occur. Fault isolation is implemented by a set of detection subspaces associated with every different fault which is assigned to its own detection subspace. The residual is not only sensitive to the fault, but also has a robustness against the non-decoupling disturbance and uncertainties in finite frequency. Simulation examples are given to demonstrate the effectiveness of this method.
In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.
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