对非线性车辆的自适应后退控制,可保证弦稳定性和抑制级联波动
IEEE transactions on cybernetics
|September 25, 2025
概括
这项研究引入了一个新的控制框架,以减轻联网和自动化车辆 (CAV) 的"蝶效应". 这种方法通过抑制距离,速度和加速度的波动以提高稳定性来确保更顺的车辆动态.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 汽车工程 汽车工程
背景情况:
- 连接和自动化车辆 (CAV) 在排队控制方面取得了进展.
- 现有的方法往往忽略了这些问题.
- 蝶效应是一种蝶效应.
- 这导致非线性排队的不可预测波动.
- 单独的车辆和弦稳定性并不能防止不舒服的速度和加速度变化.
研究的目的:
- 为CAV中队提出一个平行错误波动抑制控制框架.
- 解决和减轻不可预测的情况.
- 蝶效应是一种蝶效应.
- 在非线性车辆排队中.
- 为了确保稳定和舒适的车辆运动,尽管潜在的间距变化.
主要方法:
- 开发可调整的三层误差边界,用于距离,速度和加速.
- 整合一个加强的Barbalat-lemma过补偿机制.
- 适应性方法的应用,使用辐射基函数神经网络 (RBFNNs) 进行非对称错误跟踪.
- 建议一个自适应的后退控制,整合主动和反应性抑制策略.
主要成果:
- 拟议的框架有效地将传播错误限制在预定义的包裹中.
- 非对称错误跟踪主动地抑制了车辆动态中的潜在波动.
- 适应式后退控制减轻了无法量化的情况.
- 蝶效应是一种蝶效应.
- . . . . . . . . . . . . . . 这就是为什么我会这样做.
结论:
- 开发的控制方法显著提高了CAV中队的稳定性和舒适性.
- 理论分析和模拟验证了拟议方法的有效性和优越性.
- 这项工作为管理非线性车辆排队中的复杂动态提供了强大的解决方案.
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