没有模型和伪反向的张神经动力学方案用于机器人手臂的路径跟踪控制
IEEE transactions on neural networks and learning systems
|March 18, 2025
概括
本研究介绍了一种新的无模型和伪反向的张神经动力学 (ZN) 方案,用于机器人手臂路径跟踪. 这种方法提高了准确性,并通过避免复杂的雅可比矩阵计算来简化控制.
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
- 计算神经科学是一种神经科学.
背景情况:
- 路径跟踪控制对于机器人手臂至关重要,但准确的建模具有挑战性.
- 越来越多地研究无模型控制方案以克服建模困难.
- 现有的无模型方法通常依赖于雅可比矩阵估计,这可能引入错误.
研究的目的:
- 提出一个基于张神经动力学 (ZN) 的新型估计器,用于雅可比矩阵的伪反向 (PI).
- 引入一个新的无模型和无PI ZN (MFPIFZN) 方案用于机器人手臂路径跟踪.
- 为了提高控制精度和减少机器人手臂路径跟踪的操作复杂性.
主要方法:
- 开发一种基于ZN的新型估计器,用于雅科比矩阵的PI.
- 关于消除PI计算需求的MFPIFZN方案的建议.
- 理论分析以保证拟议方案的趋同性和稳定性.
- 在平面四环和Kinova Jaco2机器人手臂上的实验验证.
主要成果:
- 多重投资型区域网络计划显著降低了运营复杂性.
- 拟议的方案通过避免PI计算错误来提高路径跟踪的准确性.
- 实验结果表明,在不同的机器人手臂平台上表现出色.
- 对比实验证实了MFPIFZN方案的优越性,而不是其他无模型方法.
结论:
- MFPIFZN方案为机器人臂的无模型路径跟踪控制提供了强大而高效的解决方案.
- 这种新的方法简化了实施,同时提高了性能.
- 该研究通过严格的实验验证实了拟议方法的有效性和优越性.
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