适应式滑行方向盘控制方法用于机器人在不确定的倾斜平面上,具有冗余的承载能力.
Lin Zhang1, Baoyu Wang2, Enguang Guan3
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Biomimetics (Basel, Switzerland)
|February 23, 2024
概括
本研究介绍了爬制造机器人 (CMo-Rs) 的自适应控制算法,该算法可以有效地管理各种表面的滑动. 这种新方法确保了卓越的轨迹跟踪精度,提高了机器人制造能力.
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
- 制造业 制造技术 制造技术
背景情况:
- 爬行制造机器人 (CMo-Rs) 为大型组件制造提供了革命性的潜力.
- 对于CMo-R,运动控制面临着重大挑战,特别是在各种表面的防滑方面.
- 现有的控制方法可能无法充分解决不确定的倾斜平面的复杂性.
研究的目的:
- 为CMo-Rs设计和验证一个适应性动力学控制算法,以解释滑动现象.
- 提高CMo-Rs在各种地形,包括倾斜和垂直平面上的轨迹跟踪精度和稳定性.
- 为了证明拟议的滑动估计控制对传统PID控制的优越性.
主要方法:
- 基于磁吸附的CMo-R的设计,具有冗余的移动性.
- 开发一个包含滑动动态的四轮运动模型.
- 使用莱普诺夫理论进行滑动估计的自适应动力学控制算法的实现.
- 与传统的PID控制器进行比较的数值模拟和实验验证.
主要成果:
- 拟议的自适应控制算法显著提高了水平,倾斜 (50°,60°) 和垂直平面上的轨迹跟踪精度.
- 该算法即使在不确定的倾斜平面上也表现出强大的性能,超过了传统的PID控制器.
- 实验结果验证了开发的控制方法的可行性,适用性和稳定性.
结论:
- 适应性动力控制算法有效地解决了CMo-Rs中的反滑动挑战.
- 开发的方法提高了机器人制造精度和适用于各种环境中的大型复杂元件的适用性.
- 这项研究推进了用于工业制造的爬行机器人的运动控制.
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