用机器学习控制复杂系统到从未见过的动态目标态，突破传统方法限制。
Controlling Dynamical Systems into Unseen Target States Using Machine Learning
- 基于参数感知的下一代储备池计算，实现未知参数下的系统行为预测。
- 成功实现从周期到混沌等根本性动态转变的快速、无瞬态过渡。
- 适用于非平稳控制场景，尤其适合电力系统等易崩溃复杂系统。
我们提出一种新型、无需模型且数据驱动的方法，用于将复杂动力系统控制至此前未见过的目标状态，包括动态特性显著不同甚至复杂的状态。借助下一代储备池计算（NGRC）的参数感知实现，该方法能准确预测在未观测参数区间内的系统行为，进而通过新的预测评估与选择机制，实现对任意目标状态的控制。关键在于，该方法可处理从周期性到间歇性或混沌行为的根本性动态变化。其参数感知特性支持非平稳控制，控制方案依据预设目标生成并评估。除了证明该方法在扩展至混沌目标态时实现无瞬态控制和过渡时间外，我们在非线性电力系统模型上验证了其有效性。该方法在系统频繁发生崩溃的场景中仍能成功引导过渡，确保快速响应并避免长期瞬态过程。该方法将基于机器学习的控制能力拓展至此前无法触及的目标动态，为新控制应用开辟道路，同时保持极高效率。
原文摘要 · Abstract (English)
We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of next-generation reservoir computing (NGRC), our approach accurately predicts system behavior in unobserved parameter regimes, enabling control over transitions to arbitrary target states utilizing a new prediction evaluation and selection scheme. Crucially, this includes states with dynamics that differ fundamentally from known regimes, such as shifts from periodic to intermittent or chaotic behavior. The method's parameter awareness facilitates non-stationary control with which control scenarios are generated and evaluated on the basis of predefined control objective. In addition to proving the method for transient-free control to extrapolated chaotic target states over transition times, we demonstrate the method's effectiveness on a nonlinear power system model. Our method successfully navigates transitions even in scenarios where system collapse is observed frequently, while ensuring fast transitions and avoiding prolonged transient behavior. By extending the applicability of machine learning-based control mechanisms to previously inaccessible target dynamics, the methodology opens the door to new control applications while maintaining exceptional efficiency.
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