循环神经网络用于操纵器的轨迹跟踪控制,其质量矩阵未知
Jian Li1, Junming Su1, Weilin Yu1
1College of Information Technology, Jilin Agricultural University, Changchun, China.
本研究介绍了用于机器人操纵器控制的循环神经网络 (RNN),有效处理不确定性和未知的动态,以精确跟踪轨迹. 这种新的方法整合了动力学和动态模型,在现实世界机器人操作中提供了卓越的性能.
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
- 机器学习 机器学习
背景情况:
- 现实世界中的机器人操作容易产生不确定性,影响操纵器控制的准确性.
- 精确的轨迹跟踪对于有效的机器人操纵至关重要,但在不确定的条件下具有挑战性.
研究的目的:
- 为准确的操纵器轨迹跟踪提出一种新的循环神经网络 (RNN) 方法.
- 解决机器人系统中的不确定性,特别是未知的质量矩阵.
主要方法:
- 一个动力控制器被设计用于计算所需的关节加速与错误反.
- 开发了一个循环神经网络 (RNN),将动力控制与机器人的动态模型和质量矩阵估计器集成在一起.
- 在Franka Emika Panda操纵器上进行了理论分析和模拟实验.
主要成果:
- 拟议的RNN方法有效地处理系统的不确定性,并实现准确的轨迹跟踪.
- 理论分析证实了RNN的学习和控制能力.
- 与现有方法相比,模拟实验证明了拟议方法的有效性和优越性.
结论:
- 综合RNN方法为在不确定的环境中操纵器控制提供了强大的解决方案.
- 这种方法提高了机器人操作的精度和可靠性.
- 该研究验证了RNN在先进机器人控制应用中的潜力.
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