神经网络控制分数级混沌系统,控制方向未知
Suxia Wang1,2, Yong Chen3
1College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Heliyon
|March 6, 2024
概括
本研究介绍了一种新型的神经网络控制,用于使用Nussbaum函数处理输入和未知控制器增益的小数序混乱系统 (FOCS). 该方法确保了稳定性,没有达到阶段,通过模拟证实.
科学领域:
- 控制理论 控制理论
- 非线性动力学是一种非线性动力学.
- 计算智能是一种计算智能.
背景情况:
- 分数秩序混乱系统 (FOCS) 呈现复杂的动态.
- 传统的滑动模式控制面临着像达到阶段这样的局限性.
- 输入和和未知的控制器获得复杂的控制设计.
研究的目的:
- 为FOCSs开发一个强大的神经网络控制策略.
- 为了应对输入和和未知控制器增益信号的挑战.
- 为了克服滑动模式控制中的达到相位限制.
主要方法:
- 使用神经网络来建模系统的不确定性.
- 设计一个稳定的滑动表面,消除了达到阶段的需要.
- 使用整数顺序的Nussbaum增益控制方法.
- 开发一种具有神经网络滑动模式可变结构的新型控制器.
主要成果:
- 一个稳定的滑动表面被成功建造.
- 设计了一种基于神经网络的新型滑动模式控制器.
- 控制器有效地管理了输入和和未知的控制器增益.
- 模拟实验验证了拟议的控制方法的实用性.
结论:
- 拟议的方法为控制具有挑战性不确定性的FOCS提供了可靠的解决方案.
- 神经网络和Nussbaum功能的集成提高了控制性能.
- 这种方法推进了复杂非线性系统的控制策略.
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