Robust fault detection and isolation for dynamics of high-speed train with uncertainties based on descriptor systems

Abstract

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.

Partial eigenstructure assignment for descriptor high-order linear systems via proportional plus derivative state feedback: A parametric approach

“…The stability of a linear time-invariant (LTI) system depends on the eigenvalues of the closed-loop system and the performance characteristics of the system are determined by the eigenstructure (eigenvalues and eigenvectors) of the closed-loop system (Sobel et al, 1994; White, 1995). Hence, as an important means of control, eigenstructure assignment for normal systems (Li and Lam, 2016; Yu and Duan, 2009), descriptor systems (Duan and Patton, 1997), and descriptor quasi-linear systems (Gu and Zhang, 2020, 2021; Gu et al, 2019) have become the focus of many scholars and been applied in related practical fields (Wang et al, 2022). In addition, many other problems of control system design have been solved effectively by using eigenstructure assignment theory (Gu and Wang, 2022; Gu et al, 2022a; Padula et al, 2021).…”