自動制御系におけるニューラルネットワーク制御器の構造とパラメータ最適化
Structure and Optimization of Parameters for Neural Network Controllers in Automatic Control Systems
制御対象の模倣器を強化学習で事前構築し、制限や不完全情報下でも動作するニューラルネット制御器の構造・パラメータ合成法を提案した論文。
著者: Sergey Feofilov, Dmitry Khapkin, Andrey Kozyr, Eduard Heiss, Andrey Efromeev
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
The article outlines the methodology of structural and parametric synthesis of neural network controllers for controlling objects with limiters under incomplete information about the controlled object. Artificial neural networks are used to create controllers that are sequentially integrated into a control system with control objects. Reinforcement learning and pre-building a neural network imitator of the control object are used to synthesize the neural network controller. This approach is particularly effective when classical control system synthesis methods are not applicable due to significant nonlinearity and the difficulty in forming a mathematical model of the control object with the required accuracy. The proposed methods expand the class of technical systems for which direct synthesis of near-optimal control laws is possible. The robustness, adaptability and technical feasibility of neural network controllers make them interesting for practical applications. The main attention in the article is paid to the choice of neural network structure in the imitator and controller, formation of training samples taking into account the limitations of the control object.
