在基于离线和在线融合学习的开放式变化场景中,演化操纵器的模糊控制
IEEE transactions on neural networks and learning systems
|November 11, 2025
概括
本研究介绍了机器人操纵器不断发展的控制框架,提高了它们在不断变化的环境中的准确性和适应性. 该方法结合了深层模糊神经网络和不断发展的模糊系统,以提高现实世界的性能.
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
- 人工智能的人工智能
背景情况:
- 准确的操纵器动态模型对于高性能和合规控制至关重要.
- 固定参数控制器在现实世界机器人中常见的动态,开放世界的场景中扎.
研究的目的:
- 开发一个不断变化的控制框架,以改善操纵器在不断变化的环境中的准确性和合规性.
- 整合线下和在线学习策略,用于自适应机器人控制.
主要方法:
- 一个混合框架,将离线学习的深度模糊神经网络 (DFNN) 和在线学习的演变模糊系统 (EFS) 结合起来.
- 在线学习包括间接和直接的学习方法,利用专家知识库.
主要成果:
- 在PUMA560操纵器上的模拟和在6-DOF操纵器上的实验证明了有效性.
- 拟议的方法显著提高了跟踪控制的准确性,并减少了反控制扭矩.
结论:
- 不断变化的控制框架提高了操纵器在动态环境中的适应性.
- 这种方法为现实世界机器人控制挑战提供了强大的解决方案.
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