随机神经网络控制随机非线性系统与二次局部不对称规定的性能
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究介绍了用于使用随机神经网络防止内存溢出的随机非线性系统的自适应神经网络控制. 这种新的方法确保了固定时间稳定性和在性能限制内精确的跟踪.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 现有的适应性随机控制方法与复杂的随机环境作斗争,由于确定性近似,经常面临内存溢出问题.
- 以前规定的性能控制方案缺乏有效抑制输入振动和优化输出超标和稳定状态误差偏差的机制.
研究的目的:
- 开发一种适应性神经网络控制方案,为随机非线性系统提供规定的性能.
- 解决确定性神经网络在近似随机非线性项方面的局限性,并解决内存溢出问题.
- 引入一种新的规定的性能设计,增强过渡性和稳定性特性,并确保固定时间稳定性.
主要方法:
- 采用随机神经网络来近似随机非线性术语,克服内存溢出问题.
- 开发了一种新的规定的性能设计,集成二次和局部不对称的特征,以抑制振动和优化错误.
- 在固定时间框架内实施控制方案,以保证闭环系统的概率固定时间限制.
主要成果:
- 拟议的随机神经网络方法有效地解决了适应性随机控制中的内存溢出问题.
- 这种新型规定的性能方法成功地抑制了短暂的输入振动,并优化了输出超标和稳定状态误差偏差.
- 固定时间框架确保所有闭环系统的概率是固定时间的,跟踪错误在预定义的范围内.
结论:
- 具有规定的性能的自适应神经网络控制方案对随机非线性系统有效.
- 使用随机神经网络和新型规定的性能设计比现有方法提供了显著的改进.
- 固定时间的趋同保证提高了控制系统的稳定性和可靠性.
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