Fault diagnosis for a class of nonlinear systems via ESO.
In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach.
Yan
Bingyong
B
Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. yanby@sjtu.edu.cn
Tian
Zuohua
Z
Shi
Songjiao
S
Weng
Zhengxin
Z