In this paper, an adaptive sliding mode neural network(NN) control method is investigated for input delay tractor-trailer system with two degrees of freedom. An uncertain camera-object kinematic tracking error model of a tractor car with n trailers with input delay is proposed. Radial basis function neural networks(RBFNNs) are applied to approximate the unknown functions in the error model. A sliding mode surface with variable structure control is designed by using backstepping method. Then, an adaptive NN sliding mode control method is thus obtained by combining Lyapunov-Krasovskii functionals. The controller realizes the global asymptotic trajectories tracking of the kinematics system. The stability of the closed-loop system is strictly proved by the Lyapunov theory. Matlab simulation results demonstrate the feasibility of the proposed method.

中文翻译：

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基于RBF神经网络的具有输入延迟的拖拉机-挂车系统自适应反步滑模控制

本文研究了一种自适应滑模神经网络（NN）控制方法，用于具有两个自由度的输入延迟牵引拖车系统。提出了具有输入延迟的具有n个挂车的牵引车不确定相机-目标运动跟踪误差模型。径向基函数神经网络（RBFNNs）用于逼近误差模型中的未知函数。采用反步法设计了具有变结构控制的滑模面。然后，通过结合Lyapunov-Krasovskii泛函，从而获得自适应NN滑模控制方法。控制器实现了运动学系统的全局渐近轨迹跟踪。闭环系统的稳定性由李雅普诺夫理论严格证明。Matlab 仿真结果证明了该方法的可行性。