通过基于神经网络的同步实现多关节机器人操纵器的自适应性容错跟踪控制
1Robotics and Autonomous Systems Group, Department of Design, Manufacturing and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了使用同步和神经网络的机器人操纵器的自适应性故障耐受性控制. 拟议的方法提高了机器人的性能和故障适应性,优于传统技术.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 多关节机器人操纵器需要强大的控制策略,以在故障条件下保持性能.
- 现有的耐故障控制方法可能无法充分解决不确定性和动态干扰.
- 同步技术为协调联合控制和快速减少错误提供了潜力.
研究的目的:
- 为多关节机器人操纵器提出适应性故障耐受性控制策略.
- 提高机器人减轻故障和干扰影响的能力.
- 为了在出现故障时提高机器人的整体性能和可靠性.
主要方法:
- 开发一种基于终端滑动模式控制的新,强大的同步控制策略.
- 集成神经网络以在线补偿系统不确定性,干扰和故障.
- 将同步控制与神经网络相结合,实现适应性故障耐受性.
主要成果:
- 同步技术确保了所有关节的位置错误同时趋同.
- 神经网络有效地弥补系统的不确定性,并实时适应故障.
- 在3DOF机器人操纵器上的模拟显示了与传统控制方法相比更高的性能.
结论:
- 拟议的自适应式故障耐受性控制提高了机器人操纵器对故障的弹性.
- 同步控制和神经网络的组合为故障耐受性提供了一个强大的方法.
- 这种方法确保机器人可靠运行,即使发生故障.
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