使用向后调整与模型预测控制进行非线性软人工肌肉的自适应控制
概括
这项研究引入了软人工肌肉的自适应控制方法,提高了它们的建模精度. 控制器有效地管理非线性动力学,并在软机器人应用中实现精确的运动控制.
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
- 材料科学 材料科学 材料科学
背景情况:
- 软的人工肌肉为可穿戴设备和非结构化环境提供了合规性和安全性.
- 精确建模软执行器非线性仍然是一个重大挑战.
研究的目的:
- 开发一种适应性控制方法,用于软的人工肌肉.
- 为了提高软执行器动态模型的准确性.
- 在软机器人系统中实现精确的参考跟踪.
主要方法:
- 利用模型学习和向后调整模型参数.
- 完善人工肌肉的输入输出关系.
- 使用跟踪性能作为模型参数调整的指标.
主要成果:
- 拟议的控制器实现了基准跟踪,根平均平方误差 (RMSE) 低于5%.
- 在不同的硬度水平和不同的负载条件下有效的性能.
- 证明了捕捉软肌肉非线性和适应不断变化的环境的能力.
结论:
- 适应性控制方法有效地模拟和控制软人工肌肉.
- 这种方法提高了软机器人应用中的精度和适应性.
- 对可穿戴机器人和在非结构化环境中的操作进行了验证的有效性.
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