APECS：適応型パーソナライズ制御システムアーキテクチャ
APECS: Adaptive Personalized Control System Architecture
人間参加型制御のための新フレームワークAPECSを提案し、制御器にLipschitz・セクタ境界を課す手法と最適な損失重み付けを導出、シミュレーションで人間操作者より4.5%、制約なしNNより9%性能が向上した。
著者: Marius F. R. Juston, Alex Gisi, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An analysis of worst-case loss functions and the optimal loss function weighting is made to implement an effective training scheme. Finally, simulations are carried out to demonstrate the effectiveness of the proposed architecture. This architecture resulted in a 4.5% performance increase compared to the human operator and 9% to an unconstrained feedforward neural network trained in the same way.
