Aeroengine Component Deviation Parameters Nonlinear Estimation Using Particle Swarm Optimization

Estimation of aeroengine component deviation parameters (CDP) is an important step of aeronautical propulsion system performance-seeking control (PSC), which traditionally employs linear Kalman filter based on piecewise state variable model (SVM). It´s not easy to get SVM, so an idea of nonlinear parameter estimation was introduced using the nonlinear aeroengine model directly. The nonlinear estimation model is established according to aeroengine operation balance and the measurable parameters matching. The nonlinear estimation was changed to a problem of solving high-dimension nonlinear equations set. Particle swarm optimization (PSO) with adaptive inertia weight was employed to solve the problem in order to satisfy the requirement of PSC calculation rapidly. The simulation results of a given turbofan engine show that utilizing the PSO algorithm can estimate the CPD precisely.