This paper develops a robust control methodology for a class of morphing aircraft,which is called innovative control effector（ICE） aircraft.For the ICE morphing aircraft,the distributed arrays of hundreds of shape-c...This paper develops a robust control methodology for a class of morphing aircraft,which is called innovative control effector（ICE） aircraft.For the ICE morphing aircraft,the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle.Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration,a desired control performance can not be satisfied with a fixed feedback controller.Therefore,a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed.Firstly,a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions.In the control allocation,many distributed arrays are managed in an optimal way to improve the robustness of the system.The scheme is used to an uncertain morphing aircraft model,and the simulation results demonstrate their performance.展开更多
This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom ...This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom is established.The first-order sliding mode differentiator(FSMD)is applied to the control scheme to avoid the problem of“differential explosion”.Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model,and an ANC controller is proposed based on this design idea.The stability of the proposed ANC controller is proved using Lyapunov theory,and the tracking error of the closed-loop system is semi-globally uniformly bounded.The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop(HIL)simulations.展开更多
基金supported by the National Natural Science Foundation of China(61074063)
文摘This paper develops a robust control methodology for a class of morphing aircraft,which is called innovative control effector（ICE） aircraft.For the ICE morphing aircraft,the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle.Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration,a desired control performance can not be satisfied with a fixed feedback controller.Therefore,a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed.Firstly,a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions.In the control allocation,many distributed arrays are managed in an optimal way to improve the robustness of the system.The scheme is used to an uncertain morphing aircraft model,and the simulation results demonstrate their performance.
基金funded by the National Natural Science Foundation of China(No.61573286)the Natural Science Foundation of Shaanxi Province(2019JM-163,2020JQ-218)+1 种基金the Fundamental Research Funds for the Central Universities(3102019ZDHKY07)supported by Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
文摘This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom is established.The first-order sliding mode differentiator(FSMD)is applied to the control scheme to avoid the problem of“differential explosion”.Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model,and an ANC controller is proposed based on this design idea.The stability of the proposed ANC controller is proved using Lyapunov theory,and the tracking error of the closed-loop system is semi-globally uniformly bounded.The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop(HIL)simulations.
