データから制御指向の動的構造を学習する
Learning Control-Oriented Dynamical Structure from Data
非線形制御アフィン系のベクトル場をデータから分解して学習し、状態依存リカッチ方程式に基づく安定な追従制御器を構成する手法を提案した。
著者: Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan, Marco Pavone
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Even for known nonlinear dynamical systems, feedback controller synthesis is a difficult problem that often requires leveraging the particular structure of the dynamics to induce a stable closed-loop system. For general nonlinear models, including those fit to data, there may not be enough known structure to reliably synthesize a stabilizing feedback controller. In this paper, we discuss a state-dependent nonlinear tracking controller formulation based on a state-dependent Riccati equation for general nonlinear control-affine systems. This formulation depends on a nonlinear factorization of the system of vector fields defining the control-affine dynamics, which always exists under mild smoothness assumptions. We propose a method for learning this factorization from a finite set of data. On a variety of simulated nonlinear dynamical systems, we empirically demonstrate the efficacy of learned versions of this controller in stable trajectory tracking. Alongside our learning method, we evaluate recent ideas in jointly learning a controller and stabilizability certificate for known dynamical systems; we show experimentally that such methods can be frail in comparison.
