一个基于转移学习的深度神经网络适应控制器,用于Furuta摆,受不确定干扰信号的影响
Firat Can Yilmaz1, Recep Onler2, Enver Tatlicioglu3
1Department of Mechanical Engineering, Gebze Technical University, 41400, Kocaeli, Turkey. fcyilmaz@gtu.edu.tr.
Scientific reports
|July 5, 2025
概括
这项研究引入了深度神经网络 (DNN) 适应性控制,以确保稳定的跟踪和干扰排斥. 混合学习方法可以在不确定的环境中提高实时性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 像Futura Pendulum这样的不太成熟的机械系统带来了重大的控制挑战.
- 传统的控制方法往往难以实时适应动态干扰.
研究的目的:
- 开发和验证一个新的自适应控制算法,用于Futura吊.
- 将深度神经网络 (DNN) 的自适应控制与强大的干扰拒绝集成在一起.
- 分析DNN架构对控制性能的影响.
主要方法:
- 一种混合学习结构,结合了线下DNN预训和在线体重适应.
- 基于Lyapunov的稳定性分析,以确保系统的融合和信号的局限性.
- 使用标准指数 (ISE,IAE,ITSE,ITAE) 和数值模拟的绩效评估.
主要成果:
- 拟议的控制器通过最小的聊天实现了稳定的跟踪.
- 证明了对随机干扰的强度.
- 通过数值模拟验证了适应性和提高了效率.
结论:
- 基于DNN的自适应控制算法为Futura Pendulum提供了一个强大的和高效的解决方案.
- 混合学习方法增强了实时控制能力.
- 该方法显示了在不确定和动态环境中实时实施的潜力.
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