根据自主地面车辆的控制,自适应性规定的性能路径具有未知的轮胎拐角刚度和执行器和度
Xin Zhou1, Heng Wang1, Yuhang Yang1
1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.
ISA transactions
|July 30, 2025
概括
本研究介绍了一种用于自动驾驶汽车的新型神经网络控制器,确保精确的路径跟踪. 该方法提高了控制的准确性和稳定性,尽管存在不确定性,例如轮胎度的变化.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 汽车工程中的人工智能
- 车辆动力学和稳定性 车辆动力学和稳定性
背景情况:
- 自动地面车辆 (AGV) 需要强大的路径控制系统.
- 挑战包括未知的建模错误,时间变化的拐角刚度和执行器和.
- 现有的规定的性能方法可能缺乏对过渡性和稳定性性能的定量控制.
研究的目的:
- 开发一个适应性后退控制器,用于AGV路径跟踪.
- 为了保证对跟踪错误的规定的短暂和稳定状态性能.
- 解决复合不确定性,包括未知的轮胎刚度和执行器和.
主要方法:
- 采用基于神经网络的自适应后退控制策略.
- 单调的管子功能和错误转换确保了性能极限.
- 比例积分补偿和边界估计减轻了振荡和先验知识要求.
主要成果:
- 拟议的控制器从数量上调整了超标,沉降时间和稳定状态精度.
- 追踪错误始终符合规定的性能标准.
- 消除了对未知的轮胎拐刚度系数的预先了解的需要.
结论:
- 开发的控制方案为AGV路径提供了卓越的性能和适应性.
- 它有效地处理复杂的不确定性,提高整体系统可靠性.
- 模拟结果验证了拟议方法的有效性.
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