A Neural Network-Based Adaptive Event-Triggered Control for Virtual Coupling High-Speed Trains With Unknown Parameters

IF 3.8 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2026-03-16 Epub Date: 2025-11-24 DOI:10.1002/acs.70010

Hui Zhao, Hanhong Cui, Xuewu Dai, Yuan Zhao

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引用次数: 0

Abstract

To cope with the effects of unknown resistance parameters, additional resistance, and high-frequency controller updating in the cooperation control of virtual-coupling high-speed train systems, this paper proposes a neural network-based adaptive event-triggered control scheme for trains. Firstly, with the train-to-train communication mode, a synthetic tracking error and its converted form are proposed to restrain the speed and position errors of trains based on the train model. Then, for the unknown resistance parameters and bounded additional resistance, a radial basis function neural network (RBFNN) based adaptive control scheme is investigated to realize the cooperative operation of trains. By incorporating the event-triggered mechanism, the communication source between the controller and actuator can be saved by reducing unnecessary controller updating. In addition, the stability condition of virtual coupling train systems is presented by the convergence analysis of the synthetic tracking error. Finally, simulation experiments are conducted to verify that the control scheme is able to realize cooperation of virtual coupling train systems in the presence of unknown parameters.

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基于神经网络的未知参数虚拟耦合高速列车自适应事件触发控制

针对虚拟耦合高速列车系统协同控制中未知电阻参数、附加电阻和高频控制器更新的影响，提出了一种基于神经网络的列车自适应事件触发控制方案。首先，采用列车间通信模式，基于列车模型，提出了一种综合跟踪误差及其转换形式，以抑制列车的速度和位置误差；然后，针对未知电阻参数和有界附加电阻，研究了基于径向基函数神经网络（RBFNN）的自适应控制方案，实现列车协同运行。通过引入事件触发机制，减少了不必要的控制器更新，节省了控制器与执行器之间的通信源。此外，通过对综合跟踪误差的收敛性分析，给出了虚拟耦合列车系统的稳定条件。最后进行了仿真实验，验证了该控制方案能够在未知参数存在的情况下实现虚拟耦合列车系统的协同。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.