ニューラルネットワーク制御器の高速初期化手法：マルチコプタ迎撃における画像ベースビジュアルサーボ制御の事例研究
A Fast Initialization Method for Neural Network Controllers: A Case Study of Image-based Visual Servoing Control for the multicopter Interception
システムモデルに基づき安定条件を満たすデータセットを構築することで、ニューラルネットワーク制御方策の初期訓練を高速化する手法を提案し、マルチコプタ迎撃の画像ベースビジュアルサーボ制御で有効性を検証した。
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著者: Chenxu Ke, Congling Tian, Kaichen Xu, Ye Li, Lingcong Bao
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Reinforcement learning-based controller design methods often require substantial data in the initial training phase. Moreover, the training process tends to exhibit strong randomness and slow convergence. It often requires considerable time or high computational resources. Another class of learning-based method incorporates Lyapunov stability theory to obtain a control policy with stability guarantees. However, these methods generally require an initially stable neural network control policy at the beginning of training. Evidently, a stable neural network controller can not only serve as an initial policy for reinforcement learning, allowing the training to focus on improving controller performance, but also act as an initial state for learning-based Lyapunov control methods. Although stable controllers can be designed using traditional control theory, designers still need to have a great deal of control design knowledge to address increasingly complicated control problems. The proposed neural network rapid initialization method in this paper achieves the initial training of the neural network control policy by constructing datasets that conform to the stability conditions based on the system model. Furthermore, using the image-based visual servoing control for multicopter interception as a case study, simulations and experiments were conducted to validate the effectiveness and practical performance of the proposed method. In the experiment, the trained control policy attains a final interception velocity of 15 m/s.
関連論文
- 深層特徴とFitted Q-Iterationによるビジュアルサーボの学習ビジュアルサーボ/強化学習
