## 1 Introduction
Ignoring these factors, which can degrade nominal closed-loop performance, can cause a controller design to fail to achieve the desired tracking and even lead to instability. In [21], a second-order dynamic terminal sliding mode control was proposed for a class of non-affine nonlinear systems designed for input constraints and external disturbances. By adding a power integrator and backstepping technique, [22] devised a novel finite-time attitude control scheme for a rigid spacecraft subject to actuator saturation. In [23], the authors studied flexible-joint robot systems with input saturation and investigated an adaptive fuzzy dynamic surface control approach. Adaptive fault-tolerant control (FTC) has been used far and wide [24; 25; 26]. In [27], active adaptive fault-tolerant neural control was discussed for mitigating actuator fault problems in large-scale uncertain systems. By introducing a backstepping technique to fault-tolerant control, an adaptive actuator fault compensation control was studied in [28] for a class of uncertain multi-input single-out discrete-time systems with triangular forms. In [29], hybrid fuzzy adaptive FTC was presented for a class of uncertain nonlinear systems with unmeasured states. In [30], an adaptive neural-fuzzy sliding-mode fault-tolerant control was developed for uncertain nonlinear systems to handle actuator effectiveness faults and input saturation. Currently, there are still rare conclusions about non-affine nonlinear systems that can tolerate input saturation and actuator faults. Moreover, the state constraints in the system are also an extremely significant matter. The Barrier Lyapunov function (BLF) is an effective tool to prevent the violation of constraints [31]. Adaptive control was proposed by designing a combined adaptive controller and BLF in [32; 33; 34; 35] to satisfy the output constraints. In [36], an adaptive neural control was addressed for a class of stochastic pure-feedback nonlinear time-delay systems with unknown direction control gains and full-state constraints. The work of [37] studied adaptive fuzzy tracking control-based barrier functions of uncertain nonlinear multi-input multi-output (MIMO) systems with full-state constraints; these systems have been applied to chemical processes. In [38], robust adaptive backstepping control for a class of non-affine nonlinear systems with full state constraints and input saturation was proposed. However, actuator faults have not been studied in the above literature. To sum up, in this paper, we propose an adaptive neural fault-tolerant control scheme for a UAV helicopter yaw control system that provides for actuator faults, input saturation, full-state constraints, and external disturbances. The non-affine nonlinear system is converted to an affine nonlinear system via a Taylor series expansion and a robust sliding mode filter. The unknown nonlinear function can be approximated by an RBFNN, and furthermore, the scheme deals with bounded disturbances. Next, an anti-saturation compensator is used to analyze the impact of input constraints; full-state constraints issues can be managed by combining BLF with a backstepping design technique. Then, the Lyapunov theory is applied to verify that the proposed adaptive anti-saturation tracking controller can ensure the boundedness of all signals in the closed-loop system. The remainder of this paper is organized as follows: Section 2, the yaw dynamic of helicopter is given, a novel dynamic model transformation technique and the control scheme are proposed, and simulation results are presented to show the effectiveness of the proposed technique. Section 3 draws conclusions of this paper.