Research on adaptive feedforward control method for Tiltrotor Aircraft/Turboshaft engine system based on radial basis function neural network

Abstract

The paper proposes an adaptive feedforward control approach based on a radial basis function neural network to address the issue of poor disturbance rejection capability in conventional integrated control methods for tiltrotor aircraft propulsion systems. Firstly, a comprehensive simulation model of the tiltrotor aircraft and turboshaft engine is established, and its accuracy is validated against the results of the General Tiltrotor Integrated Simulation Code. Building upon this, a cascade Proportional-integral control parameter design method based on separation principles is proposed to tackle the problem of designing control parameters for the Proportional Integral main loop structure. Subsequently, an adaptive feedforward control method that takes into account both aircraft-induced disturbances and model uncertainties for turboshaft engines is presented using a Radial Basis Function neural network. This approach includes a nonlinear estimator capable of estimating load disturbances and a Radial Basis Function adaptive feedforward controller. Furthermore, stability proof of the closed-loop system is provided based on Lyapunov function analysis. Finally, digital simulations and hardware-in-the-loop experiments are conducted based on a typical tiltrotor flight mission. The results demonstrate that the proposed method effectively mitigates the power turbine drop compared to the conventional Proportional Integral controller with collective pitch feedforward.