This article addresses the leader‐following neural network adaptive observer‐based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second‐order Euler‐Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection‐type neural network and an adaptive robust technique, a novel leader‐following saturated output‐feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady‐state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov‐based stability analysis is utilized to prove that all the tracking and state observation errors are semi‐globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.

中文翻译：

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基于饱和观察者的自适应神经网络领导者对具有n个挂车的N辆拖拉机的跟踪控制具有保证的性能

本文介绍了基于领导者的神经网络基于观察者的自适应控制，该控制以规定的性能规格对连接到n辆拖车的N辆拖拉机进行控制。为了提出该控制器，使用坐标的变化和非线性误差变换将约束的误差动力学转换为新的二阶Euler-Lagrange无约束误差动力学，该动力学继承了第i个车辆动力学模型的所有结构特性。通过组合投影型神经网络和一个自适应鲁棒技术中，一种新颖的领导者，以下的饱和输出反馈控制器被提出以迫使该我车辆跟踪具有规定瞬态和稳态特性的虚拟先导轨迹，同时降低执行器饱和风险并补偿所有未知的动力学模型参数，外部干扰，未模制的动力学和NN逼近误差。试探性地提出了饱和速度观测器，以消除对第i辆速度测量的要求，而不会出现任何不希望的峰值。利用基于Lyapunov的稳定性分析来证明所有跟踪和状态观测误差都是半全局一致的最终有界（SGUUB），并且它们收敛到小界限，包括具有指定性能的起点。最后，将显示计算机仿真以在实践中验证所提出控制器的功效。