自学模糊逻辑基于强大的控制机器人操纵器驱动BLDC电机:一个任务空间控制方法.
IEEE transactions on cybernetics
|September 24, 2025
概括
这项研究增强了机器人操纵器轨迹跟踪,尽管模型不确定性使用自适应模糊逻辑 (AFL) 来估计动态. 该控制器可确保无刷直流电机的可靠定位和稳定性.
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:
- 由无刷直流电机 (BLDC) 驱动的机器人操纵器经常面临模型不确定性的挑战.
- 精确的轨迹跟踪对于操纵器性能至关重要,需要强大的控制策略.
- 整合执行器动态 (AD) 对于提高定位灵敏度和整体可靠性至关重要.
研究的目的:
- 开发一种控制策略,使机器人终端效应器能够准确地跟踪所需的轨迹,尽管机器人模型和AD的不确定性.
- 通过考虑BLDC电机动力学来提高机器人操纵器的跟踪性能和可靠性.
- 提高闭环控制系统的效率和稳定性.
主要方法:
- 利用自组织的自适应模糊逻辑 (AFL) 框架来估计动态模型和AD中的不确定性.
- 实施了在线更新的会员函数平均值和AFL内部的差异,以准确估计不确定性.
- 开发了一种新的利亚普诺夫函数,以严格证明闭环系统的统一终极局限性.
主要成果:
- AFL框架成功估计了模型和执行器动力学的不确定性.
- 拟议的控制器在存在不确定性的情况下证明了增强的轨迹跟踪性能.
- 利亚普诺夫分析证实了闭环系统的稳定性和统一的最终边界性.
结论:
- 开发的自适应模糊逻辑控制器有效地解决了机器人操纵器中的模型不确定性.
- 该控制器提高了BLDC驱动机器人的轨迹跟踪精度和系统可靠性.
- 在双DOF平面机器人上的实验验证证证了控制器的实际适用性和有效性.
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