Intermediate parameter based distributed sensor fault-tolerant estimation for a class of nonlinear systems.

This paper investigates the estimation problem of a class of nonlinear systems with actuator and sensor faults. The primary objective is to design a distributed fault-tolerant observer which can estimate system states and actuator faults. Firstly, a distributed observer network with intermediate parameters is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of redundant sensors is set up for each distributed observer node to obtain more output measurement samples. More importantly, when some of the redundant sensors occur faults, all sensor signals will be further processed and classified by a new algorithm. An index of sensor health level is constructed to characterize the quality of the fault-free or faulty sensor signals. By using the proposed algorithm, unhealthy sensor signals will be automatically filtered out, while healthy ones will be retained. Based on the healthy sensor signals, the system states and actuator faults are estimated. Finally, an example demonstrates that the proposed method is effective. • In this article, an improved distributed observer network is designed. The actuator fault can be estimated even if the sub-observer nodes are not observable. • We set up a class of redundant sensors to obtain more output measurement samples. Then, a novel algorithm is designed to filter out low quality output information. • We introduce a novel robustness term to against the effect of faulty sensors. The actuator fault can be observed in the case of sensor fault, which does not require employing extra fault detection mechanisms. [ABSTRACT FROM AUTHOR]