安定性保証付き全層DNN適応制御のための自己教師ありメタ学習
Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees
オフラインの軌道データから自己教師ありメタ学習でDNNを事前学習し、オンラインで全層を適応更新する安定性保証付き適応制御フレームワークを提案。ドローンの風外乱下での追従性能を大幅に改善。
著者: Guanqi He, Yogita Choudhary, Guanya Shi
分類: cs.RO
原文アブストラクト
A critical goal of adaptive control is enabling robots to rapidly adapt in dynamic environments. Recent studies have developed a meta-learning-based adaptive control scheme, which uses meta-learning to extract nonlinear features (represented by Deep Neural Networks (DNNs)) from offline data, and uses adaptive control to update linear coefficients online. However, such a scheme is fundamentally limited by the linear parameterization of uncertainties and does not fully unleash the capability of DNNs. This paper introduces a novel learning-based adaptive control framework that pretrains a DNN via self-supervised meta-learning (SSML) from offline trajectories and online adapts the full DNN via composite adaptation. In particular, the offline SSML stage leverages the time consistency in trajectory data to train the DNN to predict future disturbances from history, in a self-supervised manner without environment condition labels. The online stage carefully designs a control law and an adaptation law to update the full DNN with stability guarantees. Empirically, the proposed framework significantly outperforms (19-39%) various classic and learning-based adaptive control baselines, in challenging real-world quadrotor tracking problems under large dynamic wind disturbance.
