神经网络适应预定义时间控制纯反非线性系统:关于机器人外骨系统的案例研究
Yuehua Fang1, Jianhua Zhang2, Yinguang Li3
1College of Mathematics and Computer Science, Hengshui University, Hebei, 053000, China.
Scientific reports
|February 19, 2025
概括
本研究介绍了一种用于非线性系统的新型预定义时间 (PT) 神经网络控制,提高了机器人外骨的性能. 与传统方法相比,该方法确保了更快,更受约束的融合.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 非亲缘的纯反非线性系统带来了重要的控制挑战.
- 现有的控制方法往往缺乏保证的融合时间或与状态约束作斗争.
- 机器人外骨需要精确和快速的控制,以实现有效的人类辅助应用.
研究的目的:
- 为非亲系纯反非线性系统开发一种新的预定义时间 (PT) 追踪自适应控制方法.
- 将这种控制方法应用于机器人外骨技术,解决状态约束.
- 为了提高系统的融合速度和性能,而不是传统的固定时间控制.
主要方法:
- 实现一个PT神经网络控制算法.
- 使用神经网络近似用于未知的非线性.
- 使用后退技术,障碍函数和平均值定理.
- 根据PT Lyapunov稳定性标准设计一个适应性法律.
主要成果:
- 拟议的方法确保在预先确定的时间内实现系统趋同.
- 神经网络有效地接近系统的非线性.
- 控制方法表现出比固定时间方法更好的性能.
- 模拟结果验证了在状态约束下对机器人外骨的控制有效性.
结论:
- 开发的 PT 神经网络控制算法为控制非线性系统,特别是机器人外骨架提供了强大的解决方案.
- 该方法在预先定义的时间内提供了保证的趋同,优于传统的控制策略.
- 该方法显示了对需要快速和受约束控制的真实应用的巨大潜力.
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