## 1 Introduction
Additionally, in [12], a composite control strategy which consists of a continuous terminal SMC and a disturbance observer was carried out to stabilize the attitude of the unmanned helicopter and land it on the shipboard. Model predictive control (MPC) is a control method capable of dealing with both linear and nonlinear problems, in addition to UAVs, it is also widely applied to solve the optimization problem of automatic driving. By considering the constraints on both state variables and control inputs, MPC could achieve good tracking performance under given conditions [13]. In [14], the explicit nonlinear MPC was used to build the multi-time-scale control structure, while a state observer was designed to handle disturbances and the dynamic inversion was employed to simplify the input-output relations, these algorithms were tested on a mode-scale helicopter. To deal with model uncertainties, an MPC strategy based on an adaptive law was proposed for an unmanned helicopter with two degrees of freedom in [15]. In addition to the unmanned helicopter, there were also other kinds of UAV using MPC-based control method to achieve autonomous flight, such as VTOL UAV [16] and ducted fan aircraft [17]. With the development of artificial intelligence (AI) technology, intelligent control has gained a lot of attention in the field of robot control. In the past decade, various intelligent control methods have been developed for UAV to achieve better performance of flight, for example, fuzzy-logic control [18], neural network [19] and reinforcement learning [20]. Although the utility of control methods mentioned above has made a great success in dealing with control design, the problem of handling nonlinearities, especially the flapping dynamics, external disturbances and model uncertainties together still needs further research. Feedback linearization could not deal with external disturbances, and if the model is not accurate enough, the effect will be greatly reduced. Adaptive control could not handle nonlinearity. The SMC easily generates a serious chattering phenomenon, which will cause damage to the actuators. The optimization process of MPC always needs powerful computing capabilities, which could not be satisfied in normal flight control computers. Additionally, an accurate model is also necessary to achieve good control performance. Intelligent control is usually combined with other methods, and there existsa lot of calculating. According to the above introduction, it is impossible to solve those problems simultaneously using a single control algorithm, and most researchers have been working on compound control strategies. Backstepping is one of the most widely used control methods. Due to helicopters' multivariable and underactuated characteristics, backstepping is suitable to build a cascaded control structure by introducing virtual inputs. As it is usually designed based on Lyapunov theory, the stability of the system could always be guaranteed. Due to these advantages, it has attracted great interest in recent years, some valuable research results have already been presented to improve the system robustness and stability [21; 22; 23; 24]. In [21], a novel sensor-based backstepping technique was developed to enhance the robustness of the UAV control system. Additionally, in [22], a proportional integral (PI) observer was proposed to deal with the disturbances, and the backstepping control combined with the observer were applied to achieve trajectory tracking. In [23; 24], backstepping method was used to cooperate with fast terminal SMC to realize position and attitude tracking for a quadcopter. However, model uncertainty and external disturbance could not be handled well only based on the backstepping method, thus, researchers have come up with various forms of observers to estimate them without having specific information [25; 26; 27]. In [25], a disturbance observer only considering hovering state was designed to compensate for environmental disturbance.