## 1 Introduction  
In the control field, with the rapidly expanding helicopter technology, the unmanned-aerial-vehicle (UAV) helicopter has been of wide concern in recent years; it has been applied to maritime supervision, environmental monitoring, search and rescue, agricultural and forestry protection, pipeline inspection, and aerial photography, to name just a few areas [1]. Since UAV helicopter flight control is a highly nonlinear, strongly coupled, and inherently unstable problem and subject to the uncertainties of various environments and varying flight conditions, the total dynamics of a UAV helicopter system are extremely complex, which have been decomposed into the longitudinal and lateral dynamics in [2; 3; 4], we only consider the turning movement of the UAV helicopter yaw control system under the comprehensive actions of actuator faults, input saturation, full-state constraints, and external disturbances in this paper. A linearized model cannot fulfill a global model approximation. Looking further, a nonlinear model for the yaw channel dynamics of helicopter is normally non-affine and has a control input that acts on the system in an implicit nonlinear way. As a result, it is a challenging task to determine the control input. To overcome this design difficulty for a non-affine system, the traditional approaches contain an inverse control strategy [5] that requires more accurate mathematical models. T-S fuzzy control [6], mean value theorem [7; 8; 9], which has many online adjustment parameters, and Taylor series expansion [10]. In this paper, a combination of a Taylor series expansion with a robust sliding mode differentiator is employed to convert the non-affine nonlinear system into an affine nonlinear system. Approximation-based adaptive control for multifarious nonlinear systems with unknown functions has drawn extensive research recently, and a great many schemes have been proposed. Fuzzy logic systems using linguistic information were applied to the control of unknown nonlinear systems in [11; 12]. A direct adaptive fuzzy robust control method was described in [13] to cope with the problems caused by the dynamic uncertainties in single-input and single-output (SISO) strict-feedback nonlinear systems. On the basis of an adaptive fuzzy control and backstepping technique, a robust adaptive fuzzy backstepping stabilization control strategy was developed for a class of stochastic nonlinear switched systems in [14]. To approximate unknown nonlinear functions and improve control system robustness, a radial basis function (RBF) neural network (NN) was used to approximate nonlinear functions in an active power filters dynamic model [15]. An adaptive fuzzy neural network (FNN) control scheme based on an RBFNN was proposed [16] to enhance the robustness and compensation performance of the system. In [17], a fuzzy sliding mode controller based on an RBFNN controller was achieved for a three-link robot system. To take advantage of neural network online approximation performance, dynamic learning from neural control for a class of nonlinear strict-feedback systems with predefined tracking performance attributes was put forward [18] and then employed on a third-order one-link robot. In [19], output feedback adaptive NN controls were studied for two classes of nonlinear discrete-time SISO systems with unknown control directions. By combining backstepping and dynamic surface control with adaptive fuzzy state-feedback control, an adaptive fuzzy dynamic surface control was investigated for a class of nonlinear systems subject to a fuzzy dead zone, unmodeled dynamics, and unknown control gain functions in [20]. Nevertheless, these adaptive control schemes do not account for the combined function of input saturation and actuator fault. As a result of space limitations, energy, and actuator physical performance, input saturation is ubiquitous in real-world control systems. Failures and faults are caused by actuators and sensors because of their continuous operation for long periods and unexpected external disturbances.