神经运算符用于强大的输出调节过度波形PDEs
概括
神经运算符 (NO) 通过近似反增益来加快不确定的超模局部微分方程 (PDEs) 的稳健输出调节. 这种方法比传统的PDE解决方案提供了显著的速度改进.
科学领域:
- 控制系统工程 控制系统工程
- 应用数学 应用数学 应用数学
- 机器学习 机器学习
背景情况:
- 神经运算符 (NO) 在全球范围内对函数进行近似计算,从而实现精确的空间增益函数近似计算.
- 后退控制在部分微分方程 (PDE) 系统中使用近似增益来实现闭环系统稳定性.
研究的目的:
- 为了利用NO理论在不确定的超标PDE系统中实现稳健的输出调节,使用基于后退的调节器.
- 为具有时间变化的参数的PDEs开发一种加速增强调度方法.
主要方法:
- 在系统参数和内核方程解决方案上对NO进行离线培训,以产生反收益.
- 实施一个基于后退的调节器,将NO产生的近似增益纳入其中.
- 理论证明和数值模拟来证明可靠的监管.
主要成果:
- NOs提供了高精度的近似空间增益函数.
- 训练的NO消除了在训练范围内的新系统参数解决核心方程的需要.
- 基于NO的方法比传统的PDE溶解器要快得多 (近三倍).
- 在特定的参数不确定性和可解决性条件下,实现了跟踪错误的趋同到0.
结论:
- NO 方法允许加速增强调度,以对不确定的过度波动 PDE 进行可靠的输出调节.
- 该方法表现出高计算效率和准确性,损失最小.
- 这项工作为涉及复杂PDE系统的实时控制应用开辟了道路.
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