Interval observer and unknown input observer-based sensor fault estimation for high-speed railway traction motor

In this paper, fault estimation for high-speed railway traction motor with sensor fault and disturbances is investigated based on the interval observer and unknown input observer (IO-UIO). First, the proposed method, which can completely eliminate the external disturbance, is studied based on the disturbance isolation characteristic of the unknown input observer. Then, an interval observer is constructed to deal with the nonlinear part, which sandwiched the actual system between the upper and lower bounds. Moreover, the Metzler matrix is constructed using an equivalent transformation and through the unified design based on the concept of the augmented state to form a global fault augmented model. Finally, simulation results are presented to illustrate the effectiveness and advantages of the proposed IO-UIO. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.