## 1 Introduction  
Due to its capability of vertically taking-off and landing, hovering and flying at low altitude, unmanned helicopters have a variety of applications in both military and civilian areas, such as search and rescue, surveillance and communication relay. However, it is a challenging mission to design an appropriate controller for an autonomous helicopter as its mathematical model consists of high nonlinearity and parameter uncertainties. When coming to the outdoor flight environment, the helicopter is easily influenced by the external disturbances like wind gust. Therefore, precise tracking of aggressive command in adverse environments has become one of the prevailing research topics for autonomous helicopters. In recent years, a lot of research has been undertaken to realize autonomous flight of unmanned aerial vehicles (UAVs). Different kinds of control algorithms have been applied in the flight control system. Linear control methods were very popular in the early days. The PID algorithm based on the H-infinity theory was used to control a quadrotor helicopter [1]. In [2], the Linear Quadratic Regulator (LQR) was designed for the trajectory tracking of a quadrotor based on linear model. H-infinity control was presented to enhance an UAV's capability of rejecting wind disturbances when hovering [3], and in [4], system uncertainties and nonlinear dynamics were considered in the design of attitude controller using H-infinity control strategy, and the simulation results showed that the control system worked well. However, linear control methods could not satisfy the full flight envelope. When the UAV is maneuvering or flying in adverse environments, the above methods designed based on a linear model would not be effective. Therefore, nonlinear control methods and intelligent control algorithms have been developed to solve these problems. Feedback linearization is usually applied as a state feedback to make the nonlinear system linear. After the system is linearized, it is much more convenient to design the control system with linear control algorithms. In [5], feedback linearization was used to decouple the nonlinear system of an unmanned helicopter and a sliding-mode observer was employed to estimate the external disturbances. To deal with the mismatched disturbances, feedback linearization and a novel sliding-mode control were proposed for the helicopters to enhance the robustness of system [6]. Adaptive control is widely utilized for dealing with external disturbance and parameter uncertainties. It is more like a strategy than an algorithm, which has a variety of forms to improve the adaption of system. In [7], an adaptive law is presented to deal with the model uncertainties using a neural network, and it allowed the authors to design the controller of unmanned helicopters based on a simplified model. In [8], the researchers developed an adaptive update law to estimate the unknown disturbances exerted on the forces and moments, which improved the capabilities of disturbance rejection in both inner and outer loops. In [9], a L1 adaptive law is chosen to assist the controller to obtain better robustness against disturbances and uncertainties, and they were conducted on a multirotor to achieve attitude control. Sliding mode control (SMC) aims to make the system work on the sliding mode surface whether the system is under external disturbance or model parameter variation. Different reaching laws are studied to improve the performance of dynamic response. In [10], a novel sliding mode surface was proposed to handle the mismatched external disturbances, and the controller designed based on it showed better hovering performance when the unmanned helicopter was under the mismatched disturbances. To eliminate the perturbations, the researchers proposed an integral SMC and chose a proper sliding surface to achieve trajectory tracking for an unmanned helicopter [11].