タスク・パラメータ・ネクサス：モデルベース制御のためのタスク特化型パラメータ学習
Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based Control
深層ニューラルネットワークを用いて、任意の追従タスクに対するモデルベース制御器の最適制御パラメータをオンラインで予測する手法を提案し、クアッドロータで未見タスクへの汎化性能を実証した。
著者: Sheng Cheng, Ran Tao, Yuliang Gu, Shenlong Wang, Xiaofeng Wang, Naira Hovakimyan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is introduced to predict the control parameters for any given tracking task at runtime, especially when optimal parameters for new tasks are not immediately available. To train this network, we constructed a trajectory bank with various speeds and curvatures that represent different motion characteristics. Then, for each trajectory in the bank, we auto-tune the optimal control parameters offline and use them as the corresponding ground truth. With this dataset, the TPN is trained by supervised learning. We evaluated the TPN on the quadrotor platform. In simulation experiments, it is shown that the TPN can predict near-optimal control parameters for a spectrum of tracking tasks, demonstrating its robust generalization capabilities to unseen tasks.
