自组织间隔2型模糊神经网络补偿控制基于实时数据信息和其在n-DOF操纵器中的应用
Youbo Sun1, Tao Zhao1, Nian Liu1
1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
一种新的自组织间隔类型-2模糊神经网络错误补偿 (SOT2-FNNEC) 算法增强了高精度操纵器控制. 这种先进的方法有效地抑制干扰,以获得卓越的运动控制性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 计算智能是一种计算智能.
背景情况:
- 对n度自由度 (n-DOF) 操纵器的高精度运动控制受到大量实时数据量和各种干扰的挑战.
- 现有的控制方法可能会与复杂的动态,信号干扰,时间延迟和操纵器操作中固有的基底动作斗争.
研究的目的:
- 为n-DOF操纵器提出一个强大的运动控制算法,能够处理重要的实时数据.
- 开发一个控制框架,有效地弥补各种干扰源引起的错误.
- 与现有的先进控制技术相比,实现更高的控制性能.
主要方法:
- 开发一种自组织间隔类型-2模糊神经网络错误补偿 (SOT2-FNNEC) 算法.
- 利用模糊神经网络结构和自我组织,基于控制数据进行在线模糊规则适应.
- 采用利亚普诺夫稳定理论,严格证明闭环控制系统的稳定性.
主要成果:
- SOT2-FNNEC算法证明了有效地抑制基,信号干扰和时间延迟.
- 模拟证实了算法在具有挑战性的条件下管理高精度运动控制的能力.
- 拟议的方法优于自组织的模糊错误补偿网络和传统的滑动模式变量结构控制.
结论:
- SOT2-FNNEC算法为高精度操纵器运动控制提供了强大而有效的解决方案.
- 自组织性质允许自适应控制,增强对各种干扰的强度.
- 这种方法代表了复杂机器人系统智能控制的重大进步.
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