涡扇发动机变参数鲁棒H_∞滤波器设计

As to solve the problem of performance parameters estimation of turbofan engine, under modeling error and measurement noise disturbances, there are some flaws including the low filter estimation accuracy, the slow filter convergence rate, and sensitive to uncertain measurement noise and modeling errors in Kalman filter algorithm and its extension. An approach based on the parameter-varying robust H_∞ filter technique is investigated. A robust filter, which satisfied robust H_∞ performance requirement, is developed by using affine parameter-dependent Lyapunov functions. The couping product term, between parameter-varying Lyapunov functions matrix and system coefficient matrix in parameter-dependent Linear Matrix Inequalities (LMIs), will lead to non-convex optimization problem. By introducing convex polytope technology, the problem above can be transformed into conventional LMIs constraint convex optimization problem to solve. The conservatism of Linear Parameter Varying (LPV) robust filter design is reduced, and the global solution is obtained. The simulation results of a turbofan engine showed that, compared with the extended Kalman filter, the designed filter has fast dynamic tracking speed and high filtering accuracy, with steady-state estimation error of ΔF_n less than 0.1% and relative estimation error of ΔF_n less than 2.5%. Beyond that, it can restrain modeling error and measurement noise disturbance strongly.