The dynamic model of high-speed trains (HSTs) is nonlinear and uncertain; hence, with the decrease in the running interval of HSTs, an accurate and safe train operation control algorithm is required. In this study, an adaptive output feedback trajectory tracking control method for HSTs is proposed on the basis of neural network observers. The proposed method aims to solve problems, such as the immeasurable speed, model parameter disturbance, and unknown external disturbance of HSTs. In this method, a neural network adaptive observer is designed to estimate the velocity of an HST. Another neural network model is used to approximate the model uncertainties. Moreover, a robust controller is constructed by considering the train position and velocity tracking errors. In the proposed observer/controller, the bound function of estimator errors is introduced to increase the accuracy and safety of the tracking system. Furthermore, the adaptive update value of the neural networks, output weights, and bound function are performed online. All adaptive algorithms and the observer/controller are synthesized in nonlinear control systems. The error signals of the closed-loop trajectory tracking system are uniformly and eventually bounded through a formal proof on the basis of the Lyapunov methods. Simulation examples illustrate that the proposed controller is robust and has excellent tracking accuracy for system model parameter and external disturbance.

中文翻译：

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基于神经网络观测器的具有扰动不确定性的高速列车轨迹跟踪的自适应输出反馈控制

高速列车的动力学模型是非线性且不确定的。因此，随着高速列车运行间隔的缩短，需要一种准确，安全的列车运行控制算法。在这项研究中，提出了一种基于神经网络观测器的HST自适应输出反馈轨迹跟踪控制方法。所提出的方法旨在解决诸如高速钢的不可测量的速度，模型参数扰动以及未知的外部扰动等问题。在这种方法中，设计了一个神经网络自适应观测器来估计HST的速度。另一个神经网络模型用于近似模型不确定性。此外，通过考虑列车位置和速度跟踪误差来构造鲁棒控制器。在建议的观察员/控制器中，引入估计器误差的界函数以提高跟踪系统的准确性和安全性。此外，可在线执行神经网络的自适应更新值，输出权重和绑定函数。所有自适应算法和观察者/控制器都在非线性控制系统中综合。闭环轨迹跟踪系统的误差信号是统一的，并最终在形式上证明基于Lyapunov方法的边界。仿真算例表明，所提出的控制器具有鲁棒性，对系统模型参数和外部干扰具有良好的跟踪精度。所有自适应算法和观察者/控制器都在非线性控制系统中综合。闭环轨迹跟踪系统的误差信号是统一的，并最终在形式上证明基于Lyapunov方法的边界。仿真算例表明，该控制器具有鲁棒性，对系统模型参数和外部干扰具有良好的跟踪精度。所有自适应算法和观察者/控制器都在非线性控制系统中综合。闭环轨迹跟踪系统的误差信号是统一的，并最终在形式上证明基于Lyapunov方法的边界。仿真算例表明，所提出的控制器具有鲁棒性，对系统模型参数和外部干扰具有良好的跟踪精度。