ハミルトニアン情報を用いた最適ニューラル制御と状態推定による閉ループ力学系
Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems
ハミルトニアン情報を活用したニューラルネットワークベースの最適制御器（Hion）を提案し、ポントリャーギンの最大原理に基づく予測制御と状態推定を実現した。既存のモデル予測制御と比較して優れた最適性と追従性能を示す。
著者: Josue N. Rivera, Dengfeng Sun
分類: eess.SY, cs.AI, cs.ET, cs.LG, cs.RO, cs.SY
原文アブストラクト
This paper formalizes Hamiltonian-Informed Optimal Neural (Hion) controllers, a novel class of neural network-based controllers for dynamical systems and explicit non-linear model-predictive control. Hion controllers estimate future states and develop an optimal control strategy using Pontryagin's Maximum Principle. The proposed framework, along with our Taylored Multi-Faceted Approach for Neural ODE and Optimal Control (T-mano) architecture, allows for custom transient behavior, predictive control, and closed-loop feedback, addressing limitations of existing methods. Comparative analyses with established model-predictive controllers revealed Hion controllers' superior optimality and tracking capabilities. Optimal control strategies are also demonstrated for both linear and non-linear dynamical systems.
