IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 2, NO. 1, JANUARY 2015 19 Adaptive Backstepping Tracking Control of a 6-DOF Unmanned Helicopter Bin Xian, Jianchuan Guo, and Yao Zhang Abstract—This paper presents an adaptive backstepping con- trol design for a class of unmanned helicopters with parametric uncertainties. The control objective is to let the helicopter track some pre-defined position and yaw trajectories. In order to facilitate the control design, we divide the helicopter ′ s dynamic model into three subsystems. The proposed controller combines the backstepping method with online parameter update laws to achieve the control objective. The global asymptotical stability (GAS) of the closed-loop system is proved by a Lyapunov based stability analysis. Numerical simulations demonstrate that the controller can achieve good tracking performance in the presence of parametric uncertainties. Index Terms—Unmanned helicopter, adaptive backstepping control, trajectory tracking, parametric uncertainty. I. I NTRODUCTION C OMPARED with the fixed-wing unmanned aerial vehi- cles (UAVs), unmanned helicopters have the characters of hovering, autonomous take-off and landing vertically and multi-attitude flight. They have a wide application prospect in the field of military and civilian applications. The unmanned helicopter is a special controlled object, which is a dynamic system of 6-degree-of-freedom (DOF), underactuated, multi- input multi-output (MIMO), strong coupling and nonlinear. Consequently, the development of sophisticated and reliable unmanned helicopter flight control system has recently be- come an attractive research topic in academic communities worldwide [1] . Nowadays, unmanned helicopter control methods include linear controller, nonlinear controller and intelligent con- troller. Traditional approaches to flight control and most initial attempts to achieve autonomous helicopter flight have been developed based on linear design techniques such as proportional-integral-derivative (PID) [2] , linear quadratic reg- ulator (LQR) [3] , H ∞ [4] and gain scheduling [5] . Linear control method is effective when the dynamic system state of an unmanned helicopter is near the equilibrium point. However, when the helicopter is away from the equilibrium point or aerobatic maneuvers are performed, the performance of the control system will deteriorate greatly. Therefore, in recently years there have been a growing number of papers using nonlinear control methods to deal with unmanned helicopter Manuscript received October 10, 2013; accepted July 18, 2014. This work was supported by Natural Science Foundation of Tianjin (14JCZDJC31900). Recommended by Associate Editor Changyin Sun Citation: Bin Xian, Jianchuan Guo, Yao Zhang. Adaptive backstepping tracking control of a 6-DOF unmanned helicopter. IEEE/CAA Journal of Automatica Sinica, 2015, 2(1): 19-24 Bin Xian, Jianchuan Guo, and Yao Zhang are with the Institute of Robotics and Autonomous System, Tianjin Key Laboratory of Process Mea- surement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China (e-mail: xbin@tju.edu.cn; e-mail: gjch@tju.edu.cn; zytju221@tju.edu.cn). flight control. It is shown in [6] that approximated un- manned helicopter system with dynamic decoupling is full state linearizable by choosing positions and heading as outputs. Nonlinear backstepping trajectory tracking control design for small scale helicopters is presented in [7]. A two-time scale controller is presented in [8] by using adaptive backstep- ping technique to achieve the hover flight control of an unmanned helicopter. Robust trajectory tracking control design for unmanned helicopters is introduced in [9−10]. A position tracking control system for a UAV using robust integral of the signum of error (RISE) and neural network (NN) feedforward terms is developed in [11]. In addition to the above two methods, intelligent control has also been widely used in the autonomous control of unmanned helicopters. The control methods based on model-free fuzzy and neural networks are reported in [12−13] respectively for their successful applica- tions to autonomous flight control. This paper presents an adaptive backstepping control design for unmanned helicopters with parametric uncertainties. The proposed controller employs online parameter update laws to estimate unknown parameters associated with the helicopter ′ s dynamics of mass and moment coefficients. When paramet- ric uncertainties exist in the dynamic model, the proposed controller will be a significant improvement to the traditional exact model knowledge (EMK) control method as employed in [6−7]. We use a simplified unmanned helicopter ′ s nonlinear dynamic model for the flight control development. The main objective is to let the unmanned helicopter track a predefined position and heading reference trajectory. In order to facili- tate the control design, we divide the helicopter model into three subsystems, which are the altitude subsystem, the yaw subsystem and the horizontal subsystem. Since there is no strong coupling between the three subsystems, we can design the controllers separately. The proposed design approach is obviously different from the two-level hierarchical control scheme reported in [8, 11]. It is reasonable in that this approach is mathematically consistent with the intuitive flight notion. The global asymptotical stability (GAS) of the closed-loop error system is proved by a Lyapunov based stability analysis. Numerical simulations demonstrate that the proposed con- troller can achieve good tracking performance in the presence of parametric uncertainties. This paper is organized as follows. In the next section, the nonlinear dynamic model of the unmanned helicopter is introduced. Sections III and IV are the main body of this paper, which present the adaptive backstepping control design and the stability analysis method. Simulation results and conclusion are presented in Sections V and VI.