Online Identification of Aircraft Dynamics in the Presence of Actuator Faults

Abstract

In this paper, a multiple model-based nonlinear identification approach is introduced for a conventional aircraft in the presence of different types of actuator faults. Occurrence of actuator faults can obviously reduce the validity of a predetermined dynamic model of nonlinear systems. In such cases, use of multi-model structures can be an effective choice. However, determining the optimal validity functions of the local models in a multi-model structure is still a challenging problem. This problem becomes even more challenging in case of unpredictable faults, which are not considered in training the local models. In this paper, two effective techniques are proposed for online determination of the validity functions of local models called the Error-based Gaussian validity functions (E-GVF) and the Online Sequential Extreme Learning Machine (OSELM)-based approach. Accordingly, there is no need to employ a separate Fault Detection and Isolation (FDI) block in the proposed identification approach. Also, due to great capabilities of neural networks for modeling complex nonlinear systems, recurrent neural networks are used as the local models of the proposed multi-model structure. The obtained simulation results indicate the capability of the proposed OSELM-based multi-model structure for nonlinear identification of complex dynamic systems in the presence of both predictable and unpredictable actuator faults.

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