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Online fault accommodation control for catastrophic system failures

Prompted by the increasing demands in system reliability and survivability, fault detection and accommodation has quickly become one of the most active research areas in the intelligent control community. In this work, the necessary and sufficient conditions for system online stability under catastrophic failures have been derived based upon 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 our best knowledge of the nominal system and nominal controller. Once a fault is detected, a 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.