Quadcopter Adaptive Trajectory Tracking Control: A New Approach via Backstepping Technique

Nowadays, quadcopter unmanned aerial vehicles play important roles in several real-world applications and the improvement of their control performance has become an increasingly attractive topic of a great number of studies. In this paper, we present a new approach for the design and stability analysis of a quadcopter adaptive trajectory tracking control. Based on the quadcopter nonlinear dynamics model which is obtained by using the Euler–Lagrange approach, the tracking controller is devised via the backstepping control technique. Besides, an adaptive law is proposed to deal with the system parameterized uncertainties and to guarantee that the control input is finite. In addition, the vehicle’s vertical descending acceleration is ensured to not exceed the gravitational acceleration by making use of a barrier Lyapunov function. It is shown that the suitable parameter estimator is stable and the tracking errors are guaranteed to be asymptotically stable simultaneously. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our method to that of existing approaches. The simulation results demonstrate the effectiveness of the proposed algorithm.

[1]  Nassim Rizoug,et al.  Adaptive neural network control based on neural observer for quadrotor unmanned aerial vehicle , 2014, Adv. Robotics.

[2]  William Ipanaque,et al.  Modeling, simulation and implementation of a modified PID controller for stabilizing a quadcopter , 2016, 2016 IEEE International Conference on Automatica (ICA-ACCA).

[3]  Glen Bright,et al.  Development of an UAV for search & rescue applications , 2011, IEEE Africon '11.

[4]  Yaonan Wang,et al.  Adaptive RBFNNs/integral sliding mode control for a quadrotor aircraft , 2016, Neurocomputing.

[5]  Xun Gong,et al.  Adaptive backstepping sliding mode trajectory tracking control for a quad-rotor , 2012, Int. J. Autom. Comput..

[6]  M. Moghavvemi,et al.  Modelling and PID controller design for a quadrotor unmanned air vehicle , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[7]  Jinguo Liu,et al.  Fuzzy adaptive PD control for quadrotor helicopter , 2015, 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[8]  Paulo E. Santos,et al.  PID, LQR and LQR-PID on a quadcopter platform , 2013, 2013 International Conference on Informatics, Electronics and Vision (ICIEV).

[9]  Kamesh Subbarao,et al.  Nonlinear Model Predictive Control for Unmanned Aerial Vehicles , 2017 .

[10]  Cheng Lu,et al.  Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[11]  D. Yallappa,et al.  Development and evaluation of drone mounted sprayer for pesticide applications to crops , 2017, 2017 IEEE Global Humanitarian Technology Conference (GHTC).

[12]  Jun Li,et al.  Dynamic analysis and PID control for a quadrotor , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[13]  D. Mayne.  Nonlinear and Adaptive Control Design [Book Review] , 1996, IEEE Transactions on Automatic Control.

[14]  Geng Qingbo,et al.  Adaptive sliding mode control for quadrotor helicopters , 2014, Proceedings of the 33rd Chinese Control Conference.

[15]  Dao Phuong Nam,et al.  An Adaptive Backstepping Trajectory Tracking Control of a Tractor Trailer Wheeled Mobile Robot , 2019, International Journal of Control, Automation and Systems.

[16]  Sung-Kyung Hong,et al.  Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles , 2019, Applied Sciences.

[17]  Hossein Bolandi,et al.  Attitude Control of a Quadrotor with Optimized PID Controller , 2013, Intelligent Control and Automation.

[18]  Nguyen Thanh Binh,et al.  Robust Adaptive Backstepping in Tracking Control for Wheeled Inverted Pendulum , 2018 .

[19]  Octavio Garcia,et al.  Robust Backstepping Control Based on Integral Sliding Modes for Tracking of Quadrotors , 2014, J. Intell. Robotic Syst..

[20]  M. F. Santos,et al.  Simulation and comparison between a linear and nonlinear technique applied to altitude control in quadcopters , 2017, 2017 18th International Carpathian Control Conference (ICCC).

[21]  Khac Duc Do.  Path-Tracking Control of Stochastic Quadrotor Aircraft in Three-Dimensional Space , 2015 .

[22]  Um Rao Mogili,et al.  Review on Application of Drone Systems in Precision Agriculture , 2018 .

[23]  Saleh Mobayen,et al.  Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties. , 2017, ISA transactions.

[24]  Akhilesh Swarup,et al.  Improved Quadrotor Altitude Control Design Using Second-Order Sliding Mode , 2017 .

[25]  D. Erdos,et al.  An experimental UAV system for search and rescue challenge , 2013, IEEE Aerospace and Electronic Systems Magazine.

[26]  G. Raffo,et al.  An integral predictive / nonlinear H ∞ control structure for a quadrotor helicopter , 2009 .

[27]  Sung Kyung Hong,et al.  Simple nonlinear control of quadcopter for collision avoidance based on geometric approach in static environment , 2018 .

[28]  Zongyu Zuo,et al.  Adaptive trajectory tracking control design with command filtered compensation for a quadrotor , 2013 .

[29]  Ahmed Hassan Ahmed,et al.  Attitude stabilization and altitude control of quadrotor , 2016, 2016 12th International Computer Engineering Conference (ICENCO).

[30]  Ashfaq Ahmad Mian,et al.  Modeling and Backstepping-based Nonlinear Control Strategy for a 6 DOF Quadrotor Helicopter , 2008 .

[31]  Francis Eng Hock Tay,et al.  Barrier Lyapunov Functions for the control of output-constrained nonlinear systems , 2009, Autom..

[32]  Benyamin Kusumoputro,et al.  Design and development of heavy-lift hexacopter for heavy payload , 2017, 2017 International Seminar on Application for Technology of Information and Communication (iSemantic).

[33]  Roland Siegwart,et al.  PID vs LQ control techniques applied to an indoor micro quadrotor , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[34]  Juan Alberto Benito,et al.  Design considerations of a small UAV platform carrying medium payloads , 2014, Design of Circuits and Integrated Systems.

[35]  Sung Kyung Hong,et al.  Quadcopter Robust Adaptive Second Order Sliding Mode Control Based on PID Sliding Surface , 2018, IEEE Access.

[36]  Sung Kyung Hong,et al.  Fault-tolerant Control of Quadcopter UAVs Using Robust Adaptive Sliding Mode Approach , 2018, Energies.

[37]  Anthony Tzes,et al.  Model predictive quadrotor control: attitude, altitude and position experimental studies , 2012 .

[38]  Abdelaziz Benallegue,et al.  Backstepping Control for a Quadrotor Helicopter , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[39]  Hamid Saeed Khan,et al.  Attitude and altitude control of quadrotor by discrete PID control and non-linear model predictive control , 2015, 2015 International Conference on Information and Communication Technologies (ICICT).

[40]  Frank L. Lewis,et al.  Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics , 2009, J. Intell. Robotic Syst..

[41]  Shakir Saat,et al.  Altitude controller design for multi-copter UAV , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[42]  Akhilesh Swarup,et al.  On adaptive sliding mode control for improved quadrotor tracking , 2018 .

[43]  Aaron M. Dollar,et al.  Stability of small-scale UAV helicopters and quadrotors with added payload mass under PID control , 2012, Autonomous Robots.