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, our approach accurately predicts system behavior in unobserved parameter regimes, enabling control over transitions to arbitrary target states. 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, ensuring smooth transitions between states. By extending the applicability of machine learning-based control mechanisms to previously inaccessible target dynamics, this methodology opens the door to transformative new applications while maintaining exceptional efficiency. Our results highlight reservoir computing as a powerful alternative to traditional methods for dynamic system control.

翻译：我们提出了一种新颖的、无模型且数据驱动的方法，用于将复杂动力系统控制至先前未见的目标状态，包括那些具有显著不同且复杂动力学的状态。通过利用参数感知的下一代储备池计算实现，我们的方法能够准确预测系统在未观测参数区域的行为，从而实现对任意目标状态转换的控制。至关重要的是，这包括动力学与已知区域根本不同的状态，例如从周期性行为转变为间歇性或混沌行为。该方法的参数感知特性促进了非平稳控制，确保了状态之间的平稳过渡。通过将基于机器学习的控制机制的适用范围扩展到先前无法达到的目标动力学，该方法为变革性的新应用打开了大门，同时保持了卓越的效率。我们的研究结果突显了储备池计算作为传统动力系统控制方法的一种强大替代方案。