制御のためのニューラル同定
Neural Identification for Control
未知の非線形システムを平衡点で安定化する制御則を、自己教師あり学習でシステム同定と同時に学習する手法を提案。
著者: Priyabrata Saha, Magnus Egerstedt, Saibal Mukhopadhyay
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-link pendulum balancing and trajectory tracking, pendulum on cart balancing, and wheeled vehicle path following.
