适应性神经伪反向控制时间延迟非线性歇斯底里系统,考虑到输出约束及其应用
Yuehang Liu1, Xiuyu Zhang1, Yue Wang1
1School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China; Jilin Province International Research Center of Precision Drive and Intelligent Control, Jilin, 132012, China.
这项研究引入了适应性神经控制,用于具有歇斯底里和时间延迟的系统. 这种新的方法可以提高智能执行器和生物机器人的控制性能.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 像运动平台和生物机器人这样的实用物理系统经常表现出hysteresis非线性,时间延迟和输出约束.
- 这些因素可以显著降低控制性能,并导致系统振荡.
研究的目的:
- 为具有输出约束的时间延迟非线性歇斯底里系统开发一种自适应的神经伪反向控制方案.
- 解决复杂的歇斯底里行为和时间延迟控制系统的挑战.
主要方法:
- 构建一个新的蝶式Krasnoselskii-Pokrovskii (BKP) 歇斯底里模型,使用新的蝶式KP内核的加权叠加来描述双循环歇斯底里.
- 开发一种新的自适应伪反向控制算法,以规避直接双循环反向模型构建的复杂性.
- 建立一个新的运动控制平台,由灵活的介电弹性体驱动器驱动,用于实验验证.
主要成果:
- 拟议的BKP模型有效地描述了双循环歇斯底里.
- 适应性伪反向控制算法成功地管理了时间延迟,歇斯底里和输出约束.
- 在介电弹性体执行器平台上的实验验证证明了控制方案的有效性和可行性.
结论:
- 适应性神经伪反向控制方案为具有歇斯底里和时间延迟的复杂非线性系统提供了强大的解决方案.
- 开发的控制策略特别适用于软生物机器人和其他智能材料驱动平台的驱动控制系统.
- 该研究强调了先进的控制技术在克服现代机械电子系统的局限性方面的潜力.
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