神经操作员用于对联 PDE-ODE 系统的观察者增益和输出反控制
Malihe Abdolbaghi1, Seyed Amir Hossein Tabatabaei2, Mohammad Keyanpour3
1Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran.
Scientific reports
|August 12, 2025
概括
这项研究引入了一个新的AI框架,用于在复杂的反应扩散系统中设计观察员和控制器. 该方法使用DeepONet准确估计系统状态并优化控制,显示高效率.
科学领域:
- 控制理论 控制理论
- 应用数学 应用数学 应用数学
- 机器学习 机器学习
背景情况:
- 对于相结合的普通微分方程-局部微分方程 (ODE-PDE) 系统,特别是反应-扩散类型的系统,设计观察者和控制器会带来重大分析挑战.
- 传统方法通常需要对核心方程进行复杂的分析解决方案,从而限制了它们的适用性和效率.
研究的目的:
- 开发一种新的数据驱动框架,用于对联ODE-PDE反应-扩散系统中的观察者和控制器设计.
- 利用神经运算器架构直接近似函数空间之间的非线性映射,绕过分析解决方案的需要.
主要方法:
- 拟议的框架使用DeepONet架构,一种神经运算符,来学习输入函数和输出解决方案之间的映射.
- 一个观察者首先被设计成基于可用的测量来估计系统的状态.
- 然后使用这些估计状态来设计控制器,以实现所需的系统行为.
主要成果:
- 与Goursat方程的精确解相比,模拟结果显示了高精度和计算效率.
- 评估了关键绩效指标,包括趋同率,估计错误和控制力度.
- 一项废除研究证实了在拟议框架内单个成分的有效性.
结论:
- 开发的数据驱动框架为设计复杂的ODE-PDE系统中的观察员和控制器提供了准确且计算效率高的解决方案.
- 基于DeepONet的方法有效地处理非线性映射,为传统分析方法提供了强大的替代方案.
- 这项工作推进了神经操作员在动态系统的控制理论中的应用.
相关概念视频
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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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