Abstract: Under the condition of a short sampling period, limited resource and computational capacity, a method for designing reduced order fault detection filtering algorithm is proposed in this paper. Small perturbation theory and a Taylor series expansion about the balance point are used to linearize the nonlinear dynamics equations of space vehicles. Then the reduced order fault detection filter is designed for the linear state space equation. The problem of feasible solution of the reduced order fault detection filtering algorithm is transformed into a nonconvex optimization problem expressed in terms of linear matrix inequalities. The local optimal solution of the nonconvex optimization is achieved by an alternate mapping algorithm. The reduced order fault detection filtering algorithm is obtained under the condition that the feasible solution exists. Finally, the reduced\|order fault detection algorithm is applied to the fault detection of the yaw control system in a small scale autonomous helicopter. Simulation results are presented to show effectiveness of the proposed approach.

Key words: Space vehicle, Reduced fault detection, Nonconvex optimization, Alternate mapping