根据修改后的RBF对茶操纵器位置跟踪的自适应性可靠控制
Yu Han1,2,3, Zhiyu Song4, Wenyu Yi5
1School of Automation, Southeast University, Nanjing, 210096, China. hanyu@caas.cn.
Scientific reports
|August 21, 2025
概括
通过精确模拟死区非线性, 改进的辐射基础功能神经网络 (m-RBF) 改善了茶叶采摘机器人的控制. 这种自适应控制系统提高了实时应用的跟踪精度和稳定性.
科学领域:
- 机器人技术
- 控制系统工程
- 人工智能
背景情况:
- 传统的神经网络在操纵器控制中的输入和和精度下降.
- 模拟死亡区域的非线性对于精确控制茶叶采摘机器人系统至关重要.
研究的目的:
- 通过解决非线性和死区问题,开发精确的茶叶采摘机器人控制模型.
- 提高操纵器控制系统的跟踪精度和准确性.
主要方法:
- 使用修改的辐射基函数神经网络 (m-RBF) 和适应性定律设计了一个适应性补偿器.
- 在Simulink中实施控制方案进行模拟,并通过六轴操纵机采茶实验进行验证.
主要成果:
- 在模拟中,m-RBF有效地近似了死区非线性,显示出出色而稳定的跟踪精度.
- 在茶叶采摘实验中,拟议的控制方案获得了95.3分,几乎是传统PID控制的两倍.
- 通过m-RBF证明了更快的学习速度和避免局部最小值.
结论:
- 基于m-RBF的控制方案提供了卓越的控制精度,稳定性和自适应性.
- 这种方法非常适合实时控制应用,特别是在采茶机器人等机器人系统中.
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