Intelligent fault tolerant control using artificial neural networks

As dynamic systems become more complex, experience more rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. We investigate the fault tolerant control problem and propose an intelligent sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffering from catastrophic faults or incipient failures. The approach is to continuously monitor the system performance and identify what the system´s current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal by adding a robust term to confine the system performance within a boundary layer. At the same time, an artificial neural network is initialized and compensates for the unknown fault dynamics online. Once the online learning process converges, the control input is tuned again by using the output of the identification model and a new least upper bound for the remaining uncertainty is estimated to further reduce the tracking error. The simulation results show a significant improvement in trajectory following performance based upon the proposed intelligent control strategy