基于Lyapunov稳定性定理和backstepping方法，针对一类受输入饱和限制的单输入单输出非线性不确定系统，提出了一种考虑输入饱和的直接自适应神经网络控制算法. 采用动态面控制方法和直接自适应神经网络控制方法，避免了传统控制设计中的“计算量膨胀”问题和潜在的控制器奇异值问题. 借助一种饱和内补偿辅助系统处理系统中的输入饱和限制问题，以保证系统的稳定性和控制性能. 该算法不但保证了闭环系统信号一致最终有界，而且使系统输出能收敛到零的一个较小邻域. 以大连海事大学远洋实习船“育龙”轮为例进行仿真，验证了所提控制器的有效性.
Based on the Lyapunov stability theory and the backstepping technique, a direct adaptive neural networks controller is proposed for a class of uncertain nonlinear single-input-single-out systems in the presence of input saturation. It is shown that all signals in the closed-loop system are uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Using the dynamic surface control (DSC) technique and neural networks, the problem of explosion of complexity inherent in the conventional backstepping method is avoided. The controller’s singularity problem is removed completely by using a special property of the affine term. A stability analysis subject to the effect of input saturation constrains is conducted with the help of an auxiliary design system. Simulation studies of an application case of a Dalian Maritime University training ship YULONG are given to demonstrate effectiveness and good performance of the proposed scheme.
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