駆動不確かさを伴う非線形システムのデータ駆動型ロバスト制御合成に向けて
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
制御証明関数(CCF)を用い、モデル不確かさがあっても安定性や安全性を達成する凸最適化ベースのデータ駆動型ロバスト制御器を提案し、倒立振子のシミュレーションで検証した論文。
著者: Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron D. Ames
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
