强力无传感器交互钥匙估计用于神经学习阻抗控制飞行并行机器人与执行器和
Minglei Zhu1, Yuhui Guo2, Dawei Gong3,4
1Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China.
Scientific reports
|March 9, 2026
概括
这项研究介绍了一种新的无传感器自适应神经学习阻抗控制器,用于飞行并行机器人 (FPR). 该控制器确保稳定和符合物理相互作用,即使在执行器和.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 飞行并行机器人 (FPR) 需要精确的控制来实现符合规定的物理相互作用.
- 执行器和在实现FPRs的稳健控制方面构成了重大挑战.
- 现有的控制方法经常与系统不确定性和外部干扰作斗争.
研究的目的:
- 为FPRs开发一个无传感器自适应的神经学习阻抗控制器.
- 为了明确地解决和适应控制系统中的执行器和.
- 在各种接触条件下确保稳定且符合物理相互作用.
主要方法:
- 建立一个多无人飞行器 (UAV) 异质合作的FPR的动态模型.
- 开发了一个外部关观察器,用于估计接触诱导的扭矩.
- 基于Lyapunov的辐射基函数神经网络 (RBFNN) 阻抗控制器的设计,配备执行器和的辅助补偿系统.
主要成果:
- 拟议的控制器有效地处理系统的不确定性和干扰.
- 辅助补偿系统减轻了执行器输入和的不利影响.
- 根据拟议的控制法,FPR系统的闭环稳定性被严格保证.
结论:
- 无传感器自适应神经学习阻抗控制器为FPRs提供了合规和稳定的物理交互.
- 该方法证明了对系统不确定性和执行器和的稳定性.
- 在ADAMS-Simulink共同模拟中,在各种接触场景中验证了控制器的有效性.
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