Hemza Mekki* | Ali Djerioui | Samir Zeghlache | Abderrahmen Bouguerra
OPEN ACCESS
In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial octorotor unmanned aerial vehicle (UAV) control is proposed in presence of the disturbances and uncertainties. Adaptive control theory based on type-1 and interval type-2 Fuzzy inference systems is used to design a controller for each subsystem of the octorotor helicopter. The proposed control scheme allows avoiding the difficult modeling, guaranteeing the stability and the robustness of the system. Exponential stability of the closed loop is guaranteed by using Lyapunov theory. The performance and the effectiveness of the proposed controller, simulation results are confirmed by simulation study.
fuzzy system, adaptive control, robust control, coaxial octorotor
[1] Salih AL, Moghavvemi M, Mohamed AF. (2010). Flight PID controller design for a UAV quadrotor. Scientific Research and Essays 23(5): 3660-3667.
[2] Utkin VI. (2008). Sliding modes in control and optimization. Berlin, Germany: Springer-Verlag https://doi.org/10.1007/978-3-642-84379-2_6
[3] Besnard L, Shtessel YB, Landrum B. (2011). Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer. Journal of the Franklin Institute 349(2): 1-27. https://doi.org/10.1016/j.jfranklin.2011.06.031
[4] Das A, Lewis F, Subbaro K. (2009). Backstepping approach for controlling a quadrotor using lagrange form dynamics. J. Intell. Robot. Syst. 56(1): 127-151. https://doi.org/10.1007/s10846-009-9331-0
[5] Zemalache K, Maaref H. (2009). Controlling a drone: Comparison between a based model method and a fuzzy inference system. Appl. Soft Comput. 9(2): 553-562. https://doi.org/10.1016/j.asoc.2008.08.007
[6] Nicol C, Macnab CJB, Ramirez-Serrano A. (2008). Robust neural network control of a quadrotor helicopter. In: Proceeding of the IEEE international conference Electrical and Computer Engineering. Niagara Falls, Canada 1233-1238. https://doi.org/10.1109/CCECE.2008.4564736
[7] Peng C, Bai Y, Gong X, Gao QJ, Zhao CJ, Tian YT. (2015). Modeling and robust backstepping sliding mode control with adaptive RBFNN for a novel coaxial eight-rotor UAV. IEEE/CAA Journal of Automatica Sinica 2: 56-64. https://doi.org/10.1109/JAS.2015.7032906
[8] Wai RJ, (2007). Fuzzy sliding mode control using adaptive tuning technique, IEEE Trans. Indus. Elect 54: 586-594. https://doi.org/10.1109/tie.2006.888807
[9] Karnik NN, Mendel JM, Liang Q. (1999). Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems 7(6): 643-658. https://doi.org/10.1109/91.811231
[10] Singh A, Jha M, Qureshi MF. (2014). Design of genetically tuned interval type-2 fuzzy PID controller for load frequency control (LFC) in the un-regulated power system. AMSE Journals -2014-Series: Advances C 69(1): 85-104.
[11] Wu D, Tan W. (2006). A simplified type-2 fuzzy logic controller for real-time control. ISA Transactions 45(4): 503-510. https://doi.org/10.1016/S0019-0578(07)60228-6
[12] Castillo O, Marroquín M, Melin P, Valdez F, Soria J. (2012). Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Information Sciences 192: 19-38. https://doi.org/10.1016/j.ins.2010.02.022
[13] Alwi H, Edwards C. (2013). Fault tolerant control of an octorotor using LPV based sliding mode control allocation. In: Proceedings of the American Control Conference. Washington, 6505-6510. https://doi.org/10.1109/ACC.2013.6580859
[14] Majd S, Benjamin L, Isabelle F, Clovis F, Hassan S, Guillaume S. (2015). Fault diagnosis and fault-tolerant control strategy for rotor failure in an octorotor. In: Proceedings of the IEEE International Conference on Robotics and Automation. Seattle 5266-5271. https://doi.org/10.1109/ICRA.2015.7139933