论文标题
朝着具有致动不确定性的非线性系统的数据驱动控制合成
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
论文作者
论文摘要
现代非线性控制理论试图赋予稳定性和安全性等属性，并已成功部署在各个领域。尽管取得了成功，但模型不确定性仍然是确保基于模型的控制器转移到现实世界系统的重大挑战。本文在使用控制证书功能（CCF）的模型不确定性的情况下开发了一种数据驱动的方法来鲁棒控制合成，从而导致基于凸优化的控制器，以实现稳定性和安全性等属性。我们框架的一个重要好处是细微的数据依赖性保证，原则上可以产生不需要完全确定输入与国家关系的样本有效的数据收集方法。这项工作是在非线性控制理论和非参数学习的交集中解决重要问题的起点。我们在多种实验构型中使用倒置的倒置验证了所提出的方法。
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations.
