柔軟構造物のフレキシビリティ制御 : 第6報,スピルオーバを考慮したニューラルネットワーク制御での学習の漸近的収束性

概要

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This paper deals with trajectory control of a flexible plate handling system using the neural network learning method. The conditions for the asymptotic convergence of the feedback error learning method for each trial are obtained. The influence of vibration modes not modeled on the conditions for the asymptotic convergence is discussed. Based on the result, obtained here, we present a fast learning method for trajectory control of a flexible plate handling system. The control system consists of a low-pass filter and two neural networks. The learning method is to change the learning rate according to the convergence conditions. A one-joint robot handling flexible plate is simulated, and the simulation results show the effectiveness of the proposed conditions and learning method.
一般社団法人日本機械学会の論文
1993-12-25