Data-driven nonlinear MIMO modeling for turbofan aeroengine control system of autonomous aircraft

Abstract

The mathematical modeling problem of a general turbofan aeroengine control system used in autonomous aircraft is investigated in this paper. Unlike the thermodynamics or physical mechanism-based modeling methods in the existing literature, a pure data-driven modeling approach is presented with high accuracy via online and offline data only. This is achieved by using the nonlinear autoregressive neural network with exogenous inputs (NARX) neural network. In comparison with the existing NARX-based MIMO modeling methods, the series–parallel structure and the parallel structure are integrated for the neural network training. Faster convergence and stability as well as higher modeling accuracy are thus achieved. Another feature of this approach is that it is independent of all the components’ dynamics in the turbofan aeroengine. Simulation results are given to verify the effectiveness of the proposed modeling approach. This is further validated by experimental testing with the method applied to mini turbofan aeroengine testbed.