## 1 Introduction
Therefore, for the Multi-TRUAVs, the advanced nonlinear control schemes are required to achieve good robust performance in autonomous flight with respect to external disturbances, parametric uncertainties, unmodeled dynamics, etc. [18]. In view of strong nonlinear characteristics of the tilt rotor UAVs, nonlinear dynamic inversion method and backstepping method are applied to the flight control design. [PERSON] designed an attitude controller based on incremental nonlinear dynamic inversion technology for a tilted quadrotor UAV with a central duct [19]. [PERSON] designed a tilting quadrotor UAV and proposed a nonlinear controller based on backstepping [20]. Based on the proposed controller, the flight test of the hover mode and simulation analysis of the transition mode were completed. These two methods require an accurate mathematical model of the controlled object. However, for the tilt rotor UAVs, the precise modeling is very difficult, which limits the application of these two methods. To attenuate the influence of parametric uncertainties and external disturbances, many efforts have been devoted to designing a robust controller for the tilt-rotor UAVs. [PERSON] designed an adaptive nonlinear hierarchical controller for a quad tilt-wing UAV [21]. Uncertainties in the aircraft dynamics can be handled with the designed control scheme. However, the adaptive law was not designed with a projection algorithm, so that the estimated parameters may not be bounded. [PERSON] proposed an explicit model predictive control scheme relying on multiparameter constrained optimization for a Y-type aircraft with tiltable rotors [10; 22]. At present, model predictive control requires high airborne computing resources and is more suitable for the controlled objects with slow dynamics. As an effective method in dealing with nonlinear systems with model uncertainties, the sliding mode control (SMC) has also been used for flight control. [PERSON] proposed the fuzzy sliding mode control scheme for a tilt-rotor UAV with varying loads [23]; the verification of the control system through ground and flight test were presented. [PERSON] proposed a neural network sliding mode control method to realize attitude control of a quad tilt rotor aircraft [24]. Moreover, the simulation results were compared with other nonlinear control algorithms to show the feasibility of the proposed method. However, due to the chattering problem, the stability of the SMC is commonly obtained at the cost of sacrificing the nominal control performance. [PERSON] proposed a nonsingular terminal sliding mode controller (NTSMC) combined with neural network (NN) approximation to track the commanded trajectory for robotic airships [25]. The simulation results indicated that NN-NTSMC reduces chattering effectively and ensures faster convergence and better tacking precision against linear hyperplane-based sliding mode control. For a class of nonlinear system, [PERSON] designed a sliding mode controller with unidirectional auxiliary surfaces, and the closed loop stability was proved under the chattering-free condition [26]. By considering the above, to improve robustness without sacrificing control performance, an auxiliary system and disturbance observer can be considered in the SMC scheme design of the tilt-rotor UAVs. Besides, due to the actual actuator dynamics, the tilt rotor UAVs are constantly affected by the constraints of the inputs. However, there is little research on the flight control of the tilt-rotor UAVs that takes this practical problem into account. To deal with the input constraints, some works on saturated nonlinear control were developed. [PERSON] proposed an adaptive robust saturated control strategy for a nonlinear quad rotor system with actuator saturation [27], and an emendatory tracking error was developed to reduce the influence of control inputs on the tracking performance. [PERSON] designed a nonlinear controller for trajectory tracking of a helicopter with constraints on main thrust and fuselage attitude [28].