超声波避难障碍和全速范围混合控制,用于智能车库
Lijie Wang1,2, Xianwen Zhu1,2, Ziyi Li1,2
1School of Measurement-Control Technology and Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究建议在智能车库中为AGV停车机器人设计一个避障控制方案. 采用卡尔曼过和滑动模式观察器的混合动力电机控制系统可确保所有速度的安全和稳健运行.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
背景情况:
- 智能三维车库面临运营安全挑战,特别是自动驾驶汽车 (AGV) 停车机器人.
- 精确的避难障碍和可靠的电机控制对于安全的AGV操作至关重要.
研究的目的:
- 为 AGV 停车机器人提出一个避免障碍的测量和控制方案.
- 为AGV中使用的无刷直流电机 (BLDC) 开发全速范围混合动力控制策略.
- 提高 AGV 停车系统的运行安全性和稳定性.
主要方法:
- 使用卡尔曼波器进行高精度距离检测.
- 开发了一个具有全速范围混合动力控制的BLDC电机的数学模型.
- 实现了双闭环向量控制与光电编码器的低到中速反.
- 集成了一个滑动模式观察器 (SMO),用于高速反,以解决波形动.
主要成果:
- 模拟显示,在低到中速时,最大超速率为1.5%,响应时间为0.01秒.
- 在使用光电编码器的更高速度时,确定了显著的速度波形动.
- 实验证实,SMO显著改善高速波形动的速度,提高了系统的稳定性.
- 验证混合动力控制系统符合全速范围内的AGV控制和安全要求.
结论:
- 拟议的混合控制方案有效地管理所有运行速度的AGV停车机器人的BLDC电机转速.
- 集成SMO对于克服高速控制限制和确保系统稳定性至关重要.
- 开发的系统提高了智能车库停车解决方案的操作安全性和可靠性.
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