论文标题
SISO反馈可线化系统的数据驱动稳定
Data-driven Stabilization of SISO Feedback Linearizable Systems
论文作者
论文摘要
在本文中，我们提出了一种稳定单输入单输出反馈系统的方法，当不知道系统模型并且没有可用的数据来识别模型时。从概念上讲，我们受到Fliess的工作的极大启发，并加入智能PID控制器，本文的结果提供了足够的条件，在这些条件下，保证其方法的修改版本可以导致渐近稳定的行为。提出的结果的关键优势之一是，与没有模型（或部分模型）（例如增强学习）的其他控制系统的方法相反，不需要大量培训或大量数据。从技术上讲，我们的结果大大取决于基于近似模型的NESIC和同事在观察者和控制器设计上的工作。在此过程中，我们还与其他良好的结果建立了联系，例如高增益观察者和自适应控制。尽管我们专注于单输入单输出反馈的简单设置可线化的系统，但我们认为所提出的结果在理论上是有见地的且实际上有用的，最后几点得到了实验证据的证实。
In this paper we propose a methodology for stabilizing single-input single-output feedback linearizable systems when no system model is known and no prior data is available to identify a model. Conceptually, we have been greatly inspired by the work of Fliess and Join on intelligent PID controllers and the results in this paper provide sufficient conditions under which a modified version of their approach is guaranteed to result in asymptotically stable behavior. One of the key advantages of the proposed results is that, contrary to other approaches to controlling systems without a model (or with a partial model), such as reinforcement learning, there is no need for extensive training nor large amounts of data. Technically, our results draw heavily from the work of Nesic and co-workers on observer and controller design based on approximate models. Along the way we also make connections with other well established results such as high-gain observers and adaptive control. Although we focus on the simple setting of single-input single-output feedback linearizable systems we believe the presented results are already theoretically insightful and practically useful, the last point being substantiated by experimental evidence.
