即興軌道追従を改善するための追加ブロックとしての深層ニューラルネットワーク設計
Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking
フィードバック制御系にDNNを追加ブロックとして組み込み、任意の目標軌道への追従性能を向上させる設計指針を提示した論文。特徴量選択の指針や有効条件、学習効率向上の条件を導出している。
著者: Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loop. The goal is to achieve a unity map between the desired and the actual outputs. In previous work, the efficacy of this approach was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed DNN approach achieved an average impromptu tracking error reduction of 43% as compared to the baseline feedback controller. Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture. In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases the training efficiency can be further increased.
