灵活操纵器的自适应控制和状态错误预测使用辐射基函数神经网络和动态表面控制方法
1Chongqing Preschool Education College, Wanzhou, Chongqing, China.
PloS one
|February 26, 2025
概括
本研究提出了一种新的控制策略,用于灵活的关节操纵器,使用辐射基函数神经网络 (RBFNN) 和自适应动态表面控制 (ADSC) 来管理不确定性并提高准确性.
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:
- 灵活的关节操纵器由于固有的不确定性和外部干扰而带来了重大控制挑战.
- 现有的控制方法往往难以在动态环境中实现高精度和适应性.
研究的目的:
- 为灵活的关节操纵器开发一种新的强大的控制策略.
- 为了提高跟踪精度和系统适应性在不确定的动态和干扰下.
主要方法:
- 辐射基函数神经网络 (RBFNN) 的集成,用于近似系统动态.
- 适应性动态表面控制 (ADSC) 的应用与非线性缓冲线.
- 为实时参数和权重更新制定适应性定律.
- 利用利亚普诺夫稳定性分析来确保系统的边界性.
- 整合长短期记忆 (LSTM) 网络用于预测状态分析.
主要成果:
- 拟议的RBFNN-ADSC战略有效地接近不确定的动态,并减轻外部干扰.
- 控制参数的实时调整确保了对动态变化的稳定性.
- 利亚普诺夫稳定性分析保证半全球均边界信号,最大限度地减少跟踪错误.
- 基于LSTM的预测分析通过模拟验证了控制方法的有效性和稳定性.
结论:
- 新的控制策略在管理灵活关节操纵器中的不确定性方面表现出卓越的表现.
- 整合RBFNN,ADSC和LSTM为复杂的机器人系统提供了强大而适应性的解决方案.
- 广泛的模拟证实了拟议方法的实际适用性和有效性.
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