基于NN的Gough-Stewart平台的视觉伺服补偿控制,负载不确定
Jun Qi1, Shijie Song2,3, Yuyang Zhao3
1School of Electronic Information and Electrical Engineering, Chengdu University, Sichuan, 611730, China.
Scientific reports
|May 3, 2025
概括
这项研究引入了一种新的控制器,用于带有不确定的负载的Gough-Stewart平台. 基于图像的动态视觉服务 (IBVS) 辐射基函数神经网络 (RBFNN) 控制器提高了轨迹跟踪的准确性和稳定性.
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:
- 戈夫-斯图尔特 (GS) 平台是平行机器人,由于不确定的负载,它们容易降低性能.
- 负载不确定性引入外部干扰,影响动态合和轨迹跟踪精度.
- 现有的方法与内部系统的不确定性 (建模,制造错误) 和广泛的外部干扰作斗争.
研究的目的:
- 为面临不确定的负载的GS平台开发一种新的控制方法.
- 为了同时提高轨迹跟踪的准确性和系统的稳定性.
- 为了解决平行机器人的当前控制策略的局限性.
主要方法:
- 提出了一个基于图像的动态视觉伺服 (IBVS) 控制器,与辐射基函数神经网络 (RBFNN) 集成.
- 整合了用于视觉伺服的加速模型.
- 使用RBFNN实现了对负载干扰的实时补偿.
- 使用利亚普诺夫稳定性分析来验证控制器性能.
主要成果:
- 拟议的控制器在轨迹跟踪准确度方面取得了显著的改进.
- 控制器表现出对不确定的外部干扰的增强强性.
- 模拟证实了同时实现良好的跟踪精度和稳定性.
结论:
- 动态IBVS-RBFNN控制器有效地弥补了GS平台中不确定的负载干扰.
- 开发的方法提供了一个强大的解决方案,用于提高并行机器人的性能.
- 这种方法成功地解决了内部不确定性和外部干扰,提高了整体系统的稳定性和控制精度.
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