An iterative learning observer-based approach to fault detection and accomodation in nonlinear systems
Proceedings of the IEEE Conference on Decision and Control
This paper presents a general framework for fault detection and accommodation using iterative learning strategy. An Iterative Learning Observer (ILO) updated online by past system output errors as well as input is constructed for the purpose of fault detection. This observer is different from conventional Luenberger observer where the observer's state is only a function of the most recent input, output and the estimation error. Furthermore, using Iterative Learning (IL) approach, an automatic control reconfiguration scheme for accomodation of faults is also described. One of the main features of the proposed scheme is that the control reconfiguration is achieved automatically based only on the response of the overall systems, and the IL controller does not require a fault detection and isolation subsystems. An example is employed to verify the effectiveness of the IL observer and IL fault accomodation scheme.
Chen, Wen and Saif, Mehrdad. (2001). An iterative learning observer-based approach to fault detection and accomodation in nonlinear systems. Proceedings of the IEEE Conference on Decision and Control, 5, 4469-4474.