On the detection and accommodation of unanticipated faults

Abstract

Pn>cedures exist for rapidly accommodating certain types of faults, based on a priori specification of the' post-fault dynamics. This paper presents a methodology for accommodating the remaining unanticipated faults. For these two approaches, a tradeoff exists between the time to attaining a solution to the reconfiguration problem and the generality of the approach. Unanticipated faults are represented as unmodeled forces and torques on the vehicle. These forces and torques are estimated using a hybrid estimation/learning approach, designed for fast estimation during the initial transient when a fault occurs, with continually increasing performance as the fault information is accumulated by the learning system. Fault accommodation is achieved by a feedforward/feedback control architecture incorporating an actuator distribution system to convert desired forces into individual actuator commands. This approach is demonstrated on a simulated autonomous vehicle, where the addition of a hybrid estimation/learning capability is shown to dramatically increase performance over time.