基于观察者的RBF神经网络干扰反向边界振动控制用于输入和的欧勒-伯诺利束模型
Jiaqi Zhong1, Jing Zhang1, Xiaolei Chen1
1School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
本研究介绍了欧勒-伯努利束的新振动控制方法,使用辐射基函数 (RBF) 神经网络来管理干扰和输入和,以提高系统稳定性.
科学领域:
- 机械工程 机械工程
- 控制系统 控制系统
- 应用数学 应用数学 应用数学
背景情况:
- 欧勒 - 伯努利束系统容易受到外部干扰和输入和,从而损害其稳定性和性能.
- 现有的振动控制方法往往难以同时有效地解决边界干扰和执行器约束.
研究的目的:
- 为面对外部干扰和输入和的欧勒 - 伯诺尼束系统开发先进的振动控制策略.
- 通过适应性控制技术,提高灵活光束系统的强度和稳定性.
主要方法:
- 使用汉密尔顿原理推导非线性部分微分方程 (PDE) 模型.
- 基于神经网络的自适应辐射基函数 (RBF) 控制规律的设计,用于估计边界干扰.
- 应用后退方法结合过度波动的触角函数来处理输入和.
主要成果:
- 拟议的自适应RBF神经网络控制器有效地估计和补偿边界干扰.
- 过度触角函数成功地强制执行输入约束,防止执行器和.
- 后退框架确保了封闭循环系统在和状态下的稳定性和趋同.
结论:
- 开发的振动控制方法为欧勒 - 伯努利束系统中管理干扰和输入和提供了卓越的方法.
- 整合RBF神经网络和后退控制为复杂的动态系统提供了强大的解决方案.
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