混合集成的滑动模式和模糊的逻辑控制为全向机器人:修改的大象群群优化轨迹跟踪的轨迹跟踪
Rasha Mohammed Hussein1, Auday Shaker Hadi1, Sameh Fareed Hasan1
1College of Mechanical Engineering, University of Technology-Iraq, Baghdad, Iraq.
Scientific reports
|October 13, 2025
概括
这项研究引入了一种用于自主机器人的混合控制框架,增强了轨迹跟踪. 这种新的方法显著减少了运动错误,并改善了移动机器人的定位时间.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 自主机器人系统在非线性和不确定的条件下面临轨迹跟踪和运动控制方面的挑战.
- 现有的控制方法在动态环境中可能难以获得精度和效率.
- 强大的运动控制对于移动机器人的可靠操作至关重要.
研究的目的:
- 提出一个新的混合控制框架,集成整体滑动模式控制 (ISMC),模糊逻辑控制 (FLC) 和修改的象群优化 (MEHO).
- 为了增强三轮全向移动机器人 (TOMR) 的轨迹跟踪和强大的运动控制能力.
- 与现有方法相比,评估拟议的控制策略的性能和效率.
主要方法:
- 实施混合控制框架,将ISMC,Sugeno型FLC和MEHO算法用于参数计算.
- 一个三轮全方向移动机器人 (TOMR) 的动态和动力学建模.
- 使用MEHO算法与自适应机制来优化勘探-开采平衡和融合速度.
主要成果:
- 拟议的控制器在2秒内显著降低了位置误差 (X和Y轴) 到0.005米以下,定向误差到0.0014rad.
- 实现了 X,Y 和横跨三角形和 C 形轨迹的方向的低根平均平方误差 (RMSE).
- 与传统的基于EHO和自适应的神经滑动控制器相比,证明扭矩变化降低了多达50%,沉降时间更快了60%以上.
结论:
- 新的混合控制框架在轨迹跟踪和自主机器人强大的运动控制方面提供了卓越的性能.
- MEHO算法有效地优化了控制参数,从而提高了系统的准确性和效率.
- 模块化和基于学习的设计显示了对在复杂环境中运行的其他机器人平台的概括潜力.
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