Fault tolerant control: an intelligent sliding mode control strategy

We investigate the fault tolerant control problem and propose an intelligent online 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 the system's current state 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 corrective sliding mode control signal to confine the system performance within a boundary layer. Meanwhile, an artificial neural network is initialized and compensates for the unknown fault dynamics online. When 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.