适应式滑动模式控制包括改进的整体补偿机制,用于输入延迟的车辆排队
Yunpeng Ding1,2, Yiguang Wang1,2, Xiaojie Li1,2
1Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin 541006, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了连接汽车的新适应性控制策略,以克服输入延迟和未知的动态. 该方法提高了车辆排队的稳定性和跟踪性能,确保更安全的自动驾驶.
科学领域:
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 互联汽车面临着执行器输入延迟的挑战,可能导致车辆排队的不稳定.
- 车辆动力学往往涉及未知和因不断变化的操作条件而变化的控制系数.
- 在这些复杂的条件下,现有的控制方法可能难以保持稳定性和跟踪准确性.
研究的目的:
- 为联网汽车开发一种自适应滑动模式控制 (SMC) 策略,以应对输入延迟和未知的时间变化系数.
- 为了提高车辆排队的强度和跟踪性能.
- 确保状态错误的趋同,并实现连接车辆系统的控制目标.
主要方法:
- 改进了带有调整因子的完整补偿机制 (ICM),旨在减轻输入延迟.
- 开发了一个基于神经网络的辐射基础函数自适应更新机制 (RBFNN-AUM),以处理未知的时间变化的控制系数.
- 建议使用滑动模式技术集成ICM和RBFNN-AUM的分布式自适应控制方案.
主要成果:
- 拟议的控制方案保证了状态错误的趋同到预定义的区域.
- 综合方法有效地提高了车辆队伍对输入延迟的稳定性.
- RBFNN-AUM成功地弥补了未知的时间变化的控制系数,提高了跟踪性能.
结论:
- 与现有方法相比,开发的自适应SMC策略为联网车辆控制提供了优越的解决方案.
- 改进的ICM和RBFNN-AUM的组合在不确定和延迟的条件下提供了有效的控制.
- 数字结果验证了车队所提出的控制策略的有效性和优越性.
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