Strong Tracking Filter-Based Fault Diagnosis of Networked Control System with Multiple Packet Dropouts and Parameter Perturbations

This paper mainly discusses the fault diagnosis problem for a class of nonlinear dynamic discrete systems with parameter perturbations and network-induced packet dropouts based on a recursive strong tracking filter. Due to the limited network bandwidth, the data transmitted via the Internet from different sensors may suffer from independent packet dropouts. And then, a series of Bernoulli sequences is employed to simulate the multiple data loss rates. In the residual design process, for a strong tacking filter (STF) which appears as an enhanced extended Kalman filter by introducing a fading factor in the filter structure, both parameter perturbations and packet dropouts are considered. In addition, a small change is made in the fading factor calculating equation in order to ensure the tracking performance of STF. Taking advantage of its good robustness against sudden changes, a novel system consisting of two STF-based models is constructed and the bias between their estimated states is treated as a residual. A fault can be alarmed through the Monte Carlo simulation method opted threshold. Meanwhile, it can also be isolated by the idea of residual contributing degree. Some simulation studies are carried out on an Internet-based three-tank system to show the effectiveness of the proposed approach.