Adaptive Incremental Nonlinear Dynamic Inversion Control Based on Neural Network for UAV Maneuver

In this paper, a flight control strategy based on incremental nonlinear dynamic inversion (INDI) using properties of general flight control systems and nonlinear dynamic inversion by feeding back angular accelerations is presented. The INDI control law is based on a linearized approximation of incremental plant dynamics and reduces dependence on modeling accuracy. However, there is still an un-negligible nonlinear character of unmanned aerial vehicle (UAV) during high angle maneuvers. The main contributions of this article are 1) proposing an adaptive neural network compensation method : INDI with neural network(INDI_NN) to correctly consider the model uncertainties; 2) a quaternion based reference model which is suitable for vehicles to experience a high angle maneuver. The simulation results support the proposed control scheme in getting better tracking performance during large range of attitudes. Hence, the proposed control method makes INDI controller more practical and more suitable for high angle maneuver like Immelman Turn.

[1]  Massimiliano Mattei,et al.  Modeling and Incremental Nonlinear Dynamic Inversion Control of a Novel Unmanned Tiltrotor , 2016 .

[2]  P. L. Deal,et al.  Simulator study of stall/post-stall characteristics of a fighter airplane with relaxed longitudinal static stability. [F-16] , 1979 .

[3]  Jan Albert Mulder,et al.  Reentry Flight Controller Design Using Nonlinear Dynamic Inversion , 2003 .

[4]  Peng Lu,et al.  Stability Analysis for Incremental Nonlinear Dynamic Inversion Control , 2018 .

[5]  Florian Holzapfel,et al.  Integrated Reference Model for a Tilt-rotor Vertical Take-off and Landing Transition UAV , 2018, 2018 Applied Aerodynamics Conference.

[6]  Aaron J. Ostroff,et al.  Enhanced NDI strategies for reconfigurable flight control , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[7]  Warren E. Dixon,et al.  Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle , 2010, IEEE Transactions on Control Systems Technology.

[8]  Youdan Kim,et al.  Nonlinear Adaptive Flight Control Using Backstepping and Neural Networks Controller , 2001 .

[9]  Peter Vörsmann,et al.  Fault-tolerant nonlinear adaptive flight control using sliding mode online learning , 2012, Neural Networks.

[10]  Yan Liu,et al.  Applications of Neural Networks in High Assurance Systems , 2010, Applications of Neural Networks in High Assurance Systems.

[11]  E. van Kampen,et al.  Stability and Robustness Analysis and Improvements for Incremental Nonlinear Dynamic Inversion Control , 2018 .

[12]  Stephen P. Boyd,et al.  Linear controller design: limits of performance , 1991 .

[13]  Jan Albert Mulder,et al.  Robust Flight Control Using Incremental Nonlinear Dynamic Inversion and Angular Acceleration Prediction , 2010 .

[14]  Aaron J. Ostroff,et al.  RECONFIGURABLE FLIGHT CONTROL USING NONLINEAR DYNAMIC INVERSION WITH A SPECIAL ACCELEROMETER IMPLEMENTATION , 2000 .

[15]  Guido C. H. E. de Croon,et al.  Adaptive Incremental Nonlinear Dynamic Inversion for Attitude Control of Micro Air Vehicles , 2016 .

[16]  Florian Holzapfel,et al.  Adaptive Augmentation of Incremental Nonlinear Dynamic Inversion Controller for an Extended F-16 Model , 2019, AIAA Scitech 2019 Forum.

[17]  Frank L. Lewis,et al.  Aircraft control and simulation: Dynamics, controls design, and autonomous systems: Third edition , 2015 .

[18]  L. Sonneveldt,et al.  Nonlinear F-16 Model Description , 2006 .

[19]  Sarangapani Jagannathan,et al.  Output Feedback Control of a Quadrotor UAV Using Neural Networks , 2010, IEEE Transactions on Neural Networks.

[20]  Gary J. Balas,et al.  Flight control design using robust dynamic inversion and time-scale separation , 1996, Autom..

[21]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .