一个基于DREM的方法来识别混乱系统
Carlos Aguilar-Ibanez1, Miguel S Suarez-Castanon2, Belem Saldivar3
1Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Ciudad de Mexico 07738, Mexico.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的最小平方方法来识别混乱系统. 该技术将非线性系统转换为线性回归,使参数恢复和增强混乱分析.
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.
- 系统识别系统识别系统
背景情况:
- 混乱系统在建模和识别方面存在重大挑战.
- 现有的方法经常与混乱动态的固有非线性和复杂性作斗争.
研究的目的:
- 开发一种简单的方法来识别混乱系统的特定类别.
- 为了利用代数可观察性和可识别性进行系统分析.
主要方法:
- 一种新的最小平方方法应用于混乱系统.
- 系统输出及其衍生品用于将系统转化为一个集成器链.
- 一个高增益观察者估计系统状态和非线性术语.
- 转换后的系统以线性回归方程表示.
主要成果:
- 该方法成功地识别了混乱系统的参数.
- 该方法有效地处理非线性,将它们归入可估计的术语中.
- 最小方程方法是通过在线性回归形式重写系统来实现的.
结论:
- 提出的方法提供了一种有效的方式来识别混乱的系统参数.
- 这种技术简化了复杂的非线性动态的分析.
- 该方法适用于代数可观测和可识别的混乱系统.
相关概念视频
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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