轮式移动机器人的安全关键数据启用预测控制
Ali Can Erüst1, Fatma Yıldız Taşcıkaraoğlu1, İbrahim Beklan Küçükdemiral2
1Department of Electrical and Electronics Engineering, Mugla Sitki Kocman University, Mugla, Türkiye.
ISA transactions
|January 30, 2026
概括
这项研究提高了机器人导航使用数据启用预测控制 (DeePC) 对于未知的系统. 适应性规范化在复杂环境中提高了20.2%的轨迹跟踪精度.
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
- 人工智能的人工智能
背景情况:
- 移动机器人需要强大的控制来执行安全关键的任务,例如避开障碍物.
- 数据支持的预测控制 (DeePC) 为线性系统提供了一个无模型的方法.
- 为非线性系统调整DeePC并确保安全是重大挑战.
研究的目的:
- 为具有未知的动态的轮式移动机器人开发安全关键的规划框架.
- 增强非线性系统数据启用预测控制的稳定性和准确性.
- 在复杂的环境中保证系统稳定性和有限的轨迹跟踪.
主要方法:
- 应用了局部线性近似与在线数据更新,以适应DeePC用于非线性系统.
- 在DeePC成本函数中集成了一个基于Lasso的自适应规范化术语,以提高稳定性.
- 集成的离散时间控制屏障功能在四级编程安全过器内,以提高安全性.
主要成果:
- 在模拟中,自适应规范化将轨迹跟踪误差降低了大约20.2%.
- 拟议的框架证明了实际的稳定性和有限的轨迹跟踪.
- 在ROS-Gazebo实时模拟验证了方法的有效性.
结论:
- 开发的DeePC框架与自适应规范化和安全过是有效的现实世界机器人导航.
- 这种方法提高了机器人在未知的复杂环境中工作的安全性和性能.
- 该方法为机器人安全关键控制应用提供了有前途的解决方案.
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