3D Trajectory Tracking of PX4 Quadrotor Based on Improved Dynamic Inversion

Journal of System Simulation ›› 2015, Vol. 27 ›› Issue (9): 1997-2007.

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3D Trajectory Tracking of PX4 Quadrotor Based on Improved Dynamic Inversion

Hu Yueli^1,2, Cai Weiping¹, Yang Wenrong²

1. College of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China;
2. Microelectronic Research and Development Center, Shanghai University, Shanghai 200072, China

Received:2015-05-15 Revised:2015-07-10 Online:2015-09-08 Published:2020-08-07
About author:Hu Yueli(1959-), male, Shanghai, China, Ph.D, professor, main research directions: image processing, machine vision, chip system and design, control theory; Cai Weiping(1989-), female, Hubei, China, master graduate student, main research directions: embedded system, research and design of control system; Yang Wenrong (1969-), male, Shanghai, China, Ph.D, associate professor, main research directions: mixed signal integrated circuit design.

Abstract

Abstract: An improved dynamic inversion method was proposed for the underactuation and strong coupling in attitude variables of the quadrotors, namely, combining model reference adaptive control (MRAC) with dynamic inversion in the fast variable loop in the system. Through the establishment of dynamic model of the quadrotor and motor model, the control law of the attitude angle and position was deduced. The MATLAB simulation for the scheme was analysed and compared with conventional dynamic inversion, and also simulated in the situation that tracking a given tracjectory with a 10% weight reduction from it's load. Simulation with respect to both scheme show that the improved control structure can quickly adapt to external disturbance and mass change and have higher precision tracking performance than conventional ones.

Key words: quadrotor, trajectory tracking, dynamic inversion, MRAC

CLC Number:

V249.1

Hu Yueli, Cai Weiping, Yang Wenrong. 3D Trajectory Tracking of PX4 Quadrotor Based on Improved Dynamic Inversion[J]. Journal of System Simulation, 2015, 27(9): 1997-2007.

References

[1] Pines D, Bohorquez F.Challenges Facing Future Micro Air Vehicle Development[J]. AIAA Journal of Aircraft (S0021-8669), 2006, 2(43): 290-305.
[2] Salih A L, Moghavvemi M, Mohamed H A F, et al. Flight PID controller design for a UAV quadrotor[J]. Scientific Research and Essays(S1992-2248), 2010, 5(23): 3660-3667.
[3] Sun L, Beard R W, Pack D.Trajectory-tracking control law design for unmanned aerial vehicles with an autopilot in the loop[C]// American Control Conference (ACC). IEEE, 2014:1390-1395.
[4] Park S, Deyst J, How J P.A new nonlinear guidance logic for trajectory tracking[C]// Proceedings of the AIAA Guidance, Navigation and Control Conference. USA: AIAA, 2004: 1-16.
[5] Mellinger D, Kumar V.Minimum snap trajectory generation and control for quadrotors[C]// Proceedings of the IEEE International Conference on Robotics and Automation. IEEE,2014: 2020-2025.
[6] Salih A L, Moghavvemi M, Mohamed H A F, et al. Modelling and PID controller design for a quadrotor unmanned air vehicle[C]// Automation Quality and Testing Robotics (AQTR), 2010 IEEE International Conference on. USA: IEEE, 2010, 1: 1-5.
[7] Bouabdallah S, Noth A, Siegwart R.PID vs LQ Control Techniques Applied to an indoor micro quadrotor[C]// Intelligent Robots and Systems, 2004.
(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on (Volume:3 )(ISBN:0-7803-8463-6) ,2004:2451-2456.
[8] Das A, Lewis F, Subbarao K.Backstepping approach for controlling a quadrotor using lagrange form dynamics[J]. Journal of Intelligent and Robotic Systems(S0921-0296), 2009, 56(1/2): 127-151.
[9] Bouadi H, Bouchoucha M, Tadjine M.Sliding mode control based on backstepping approach for an UAV type-quadrotor[J]. Proceedings of World Academy of Science: Engineering & Technolog(S0033-2097), 2007, 20(6): 22-27.
[10] Alexis K, Nikolakopoulos G, Tzes A.Model predictive quadrotor control: attitude, altitude and position experimental studies[J]. Control Theory & Applications, IET(S1751-8644), 2012, 6(12): 1812-1827.
[11] Das A, Subbarao K, Lewis F.Dynamic inversion with zero-dynamics stabilisation for quadrotor control[J]. IET control theory & applications(S1751-8644), 2009, 3(3): 303-314.
[12] Johnson E, Calise A, Corban J E.A six degree-of-freedom adaptive flight control architecture for trajectory following[C]// Proceedings of the AIAA Guidance, Navigation, and Control Conference. USA: AIAA, 2002.
[13] Pu Y F, Zhou J L, Zhang Y, et al. Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach[J]. IEEE Transactions on Neural Networks and Learning Systems(S2162-237X), 2015, 26(4): 653-662.

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3D Trajectory Tracking of PX4 Quadrotor Based on Improved Dynamic Inversion

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