改进了使用准凸函数和神经网络辅助时间延迟估计的自适应滑动模式控制,用于机器人操纵器
Jin Woong Lee1, Jae Min Rho2, Sun Gene Park2
1Department of ICT Convergence Engineering, Soonchunhyang University, Asan 31538, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了机器人操纵器的自适应滑动模式控制,使用神经网络和一种新的连续增益功能来减少聊天并确保稳定性. 该方法有效地抑制振动,并保证机器人系统的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 机器人操纵器需要精确控制复杂的任务.
- 传统的控制方法往往会受到喋喋不休和不稳定的影响.
- 现有的时间延迟估计 (TDE) 技术可能会产生影响性能的错误.
研究的目的:
- 为机器人操纵器开发一种自适应的滑动模式控制 (SMC) 策略.
- 使用神经网络增强时间延迟估计 (TDE) 并减轻TDE错误.
- 引入基于类凸函数的连续控制增益来抑制聊天.
主要方法:
- 实施了适应性SMC战略,使用神经网络增强的TDE.
- 使用辐射基础功能神经网络,具有减轻重量更新规律,以补偿TDE错误.
- 提出了基于类凸函数的连续增益来取代传统的切换增益.
主要成果:
- 提出的连续增益函数有效地抑制了聊天现象.
- 适应性控制策略保证了统一的最终界限性,确保了系统的稳定性.
- 模拟和实验结果验证了拟议方法的有效性.
结论:
- 新的自适应滑动模式控制策略通过减少聊天来提高机器人操纵器的性能.
- 集成神经网络用于TDE补偿和准凸函数用于增强控制提供了一个强大的解决方案.
- 该研究表明,在稳定和精确的机器人控制方面取得了重大进展.
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