Reconfigurable control system design for fault diagnosis and accommodation

The necessary and sufficient conditions for system online stability under catastrophic failures have been derived based upon the discrete-time Lyapunov stability theory. An online fault accommodation control framework is proposed to deal with the desired trajectory-tracking problem for systems suffering from various unknown and unanticipated catastrophic component failures. The approach is to continuously monitor the system performance and identify the system´s current state by using an efficient online fault detection scheme based upon the best knowledge of the nominal system and nominal controller. Once a fault is detected, an artificial neural network is suggested as the online estimator to learn and approximate the unknown system failure dynamics. Effective control signals to accommodate the dynamic failures are then computed through the realization of the neural network estimator for the unknown failures based only upon the partially available information of the faults. Extensive simulation studies have been completed to validate the proposed online control framework