基于辐射基函数的神经网络近似的快速非单元自适应超扭转滑动模式控制
Xiao Lin1, Junyang Li1, Yankui Song2
1State Key Laboratory of Mechanical Transmissions for Advanced Equipment, Chongqing University, Chongqing 400030, China.
ISA transactions
|August 29, 2025
概括
这项研究引入了基于神经网络的机器人关节的新型自适应控制,提高了精度和干扰排斥. 改进的快速非单元超扭转控制确保了复杂的机器人应用中的强大性能.
科学领域:
- 机器人技术
- 控制系统工程
- 人工智能
背景情况:
- 机器人关节模块需要精确控制复杂的任务.
- 在机器人控制中,非线性摩擦和刚性存在重大挑战.
- 现有的控制方案往往与奇点和外部干扰作斗争.
研究的目的:
- 为机器人关节模块开发一个改进的快速非单元自适应超扭转控制方案.
- 为了提高轨迹跟踪精度和干扰排斥能力.
- 用基于神经网络的补偿来解决机器人系统的精确控制问题.
主要方法:
- 使用拉格朗的能量方程建立了机器人关节模块的二次状态空间模型.
- 提议改进一个快速的非单点终端滑动表面,以避免单点并加速融合.
- 为不确定模型因子设计了一个辐射基础功能神经网络补偿器和一个自适应切换控制定律.
主要成果:
- 拟议的控制方案在各种参考轨迹下显示出卓越的轨迹跟踪性能.
- 在存在外部干扰的情况下显示出有效的干扰排斥能力.
- 模拟和实验结果验证了控制策略的有效性和稳定性.
结论:
- 基于神经网络的新型自适应超扭转控制方案显著提高了机器人关节模块的精确控制.
- 该方法提供了针对模型不确定性和外部干扰的强大稳定性.
- 由于无精确信息的适应性干扰排斥,工程的实际适用性得到改善.
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