In this paper, a hybrid control system called as backstepping adaptive fuzzy control system, which integrates nominal and compensation controller is developed for autonomous quadrotor helicopter with inherent time-varying disturbance. In this hybrid control system, the nominal controller based on backstepping technique is the main controller, and the compensation controller containing a fuzzy control approach is used to eliminate the effect of uncertainties caused by external disturbance. In addition, in order to relax the requirement of prior knowledge on the bound of external disturbance, an online adaptation law is derived. Asymptotical stability of the closed-loop control system is analytically proven via the Lyapunov theorem. For the problem of determining the backstepping control parameters, particle swarm optimization algorithm has been employed. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.
JafariHZarehMRoshanianJ. An optimal guidance law applied to quadrotor using LQR method. Trans Jpn Soc Aeronaut Space Sci2010; 53: 32–39.
2.
Salih AL, Moghavvemi M, Mohamed HAF, et al. Modelling and PID controller design for a quadrotor unmanned air vehicle. In: IEEE International conference on automation quality and testing robotics (AQTR), Cluj-Napoca, Romania, 2010, pp.1–5.
3.
Santos M, López V and Morata F. Intelligent fuzzy controller of a quadrotor. In: IEEE International conference on intelligent systems and knowledge engineering (ISKE), Hangzhou, China, 2010, pp.141–146.
4.
ZhangYZhaoWLuT. The attitude control of the four-rotor unmanned helicopter based on feedback linearization control. WSEAS Trans Syst2013; 12(4): 229–239.
5.
Xu R and Ozguner U. Sliding mode control of a quadrotor helicopter. In: 45th IEEE Conference on decision and control, San Diego, USA, 2006, pp.4957–4962.
6.
Bouadi H, Bouchoucha M and Tadjine M. Modelling and stabilizing control laws design based on backstepping for an UAV type-quadrotor. In: Proceedings of the IFAC symposium on intelligent autonomous vehicles, Toulouse, France, 2007, pp.245–250.
7.
Regula G and Lantos B. Backstepping based control design with state estimation and path tracking to an indoor quadrotor helicopter. Electric Eng Comput Sci 2011; 53(3–4): 151–161.
8.
Pounds P, Mahony R, Hynes P, et al. Design of a four-rotor aerial robot. In: Proceedings of Australasian conference on robotic and automation, Auckland, New Zealand, 2002.
9.
McKerrow P. Modelling the Draganflyer four-rotor helicopter. In: IEEE International conference on robotics and automation (ICRA), vol. 4, New Orleans, USA, 2004, pp.3596–3601.
10.
Bouabdallah S, Noth A and Siegwart R. PID vs LQ control techniques applied to an indoor micro quadrotor. In: IEEE/RSJ International conference on intelligent robots and systems (IROS), vol. 2453, Sendai, Japan, 2004, pp.2451–2456.
11.
CastilloPLozanoRDzulA. Stabilization of a mini rotorcraft with four rotors. IEEE Control Syst Magaz2005; 25(6): 45–55.
12.
Bouabdallah S and Siegwart R. Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. In: Proceedings of the IEEE international conference on robotics and automation, Barcelona, Spain, 2005.
13.
TayebiAMcGilvrayS. Attitude stabilization of a VTOL quadrotor aircraft. IEEE Trans Control Syst Technol2006; 14: 562–571.
14.
Madani T and Benallegue A. Backstepping control for a quadrotor helicopter. In: IEEE/RSJ International conference on intelligent robots and systems, Beijing, China, 2006, pp.3255–3260.
15.
BouadiHBouchouchaMTadjineM. Modelling and stabilizing control laws design based on backstepping for an UAV type-quadrotor. Intell Autonom Veh2007; 6: 245–250.
16.
MianAAWangD. Dynamic modeling and nonlinear control strategy for an underactuated quad rotor rotorcraft. J Zhejiang Univ Sci A2008; 9: 539–545.
17.
Al-Younes Y and Jarrah M. Attitude stabilization of quadrotor UAV using backstepping fuzzy logic & backstepping least-mean-square controllers. In: 5th IEEE international symposium on mechatronics and its applications (ISMA), Amman Jordon, 2008, pp.1–11.
18.
Fang Z, Zhi Z, Jun L, et al. Feedback linearization and continuous sliding mode control for a quadrotor UAV. In: 27th IEEE Chinese control conference (CCC), Kunming, China, 2008, pp.349–353.
19.
Alexis K, Nikolakopoulos G and Tzes A. Constrained-control of a quadrotor helicopter for trajectory tracking under wind-gust disturbances. In: 15th IEEE Mediterranean electrotechnical conference (MELECON), Valletta, Malta, 2010, pp.1411–1416.
20.
CozaCNicolCMacnabCJB. Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting. J Intell Fuzzy Syst2011; 22: 267–283.
21.
Tony CT and Mackunisy W. Robust attitude tracking control of a quadrotor helicopter in the presence of uncertainty. In: 51st IEEE Annual conference on decision and control (CDC), 2012, Maui, USA, pp.937–942.
22.
Diao C, Xian B, Gu X, et al. Nonlinear control for an underactuated quadrotor unmanned aerial vehicle with parametric uncertainties. In: 31st IEEE Chinese control conference (CCC), Hefei, China, 2012, pp.998–1003.
23.
Antonelli G, Arrichiello F, Chiaverini S, et al. Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances. In: IEEE/ASME International conference on advanced intelligent mechatronics, Wollongong, Australia, 2013, pp.1337–1342.
24.
ChenYHeYZhouM. Modeling and control of a quadrotor helicopter system under impact of wind field. Res J Appl Sci: Eng Technol2013; 6: 3214–3221.
25.
Bialy BJ, Klotz J, Brink K, et al. Lyapunov-based robust adaptive control of a quadrotor UAV in the presence of modeling uncertainties. In: IEEE American control conference (ACC), Washington, USA, 2013, pp.13–18.
26.
EscareñoJSalazarSRomeroH. Trajectory control of a quadrotor subject to 2D wind disturbances. J Intell Robot Syst2013; 70: 51–63.
27.
Kobilarov M. Trajectory tracking of a class of underactuated systems with external disturbances. In: Proceedings of the American control conference, Washington, USA, 2013, pp.1044–1049.
28.
KasaćJStevanovićSŽilićT. Robust output tracking control of a quadrotor in the presence of external disturbances. Trans FAMENA2013; 37: 29–42.
Teimoori H, Pota HR, Garratt M, et al. Helicopter flight control using inverse optimal control and backstepping. In: 12th IEEE International conference on control automation robotics & vision (ICARCV), Guangzhou, China, 2012, pp.978–983.
31.
Hassan R, Cohanim B, De Weck O, et al. A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference, Austin, USA, 2005.
32.
AllaouaBGasbaouiBMebarkiB. Setting up PID DC motor speed control alteration parameters using particle swarm optimization strategy. Leonardo Electron J Pract Technol2009; 14: 19–32.
33.
Wai R-J. Fuzzy sliding-mode control using adaptive tuning technique. IEEE Trans Ind Electron 2007; 54(1): 586–594.
34.
Voos H. Nonlinear control of a quadrotor micro-UAV using feedback-linearization. In: IEEE International conference on mechatronics (ICM), Malaga, Spain, pp.1–6.
35.
Waslander SL and Wang C. Wind disturbance estimation and rejection for quadrotor position control. In: AIAA Infotech@Aerospace conference and AIAA unmanned…Unlimited conference, Seattle, USA, 2009.
