キュービックニューラルネットワーク知的制御のロバスト性と耐故障性 : スライディングモード制御との比較・検討(機械力学,計測,自動制御)

概要

論文の詳細を見る
This study aims at establishing a robust intelligent control method with higher control performance and wider application region. In particular, this study deals with a nonlinear and failureproof control problem. In this study, the dynamical energy principle is embedded into the integrator of Cubic Neural Network (CNN) that consists of multilevel parallel processing on different degrees of abstraction. The proposed CNN is applied to a control problem of a swung up and inverted pendulum. In order to confirm the effectiveness of the integrated CNN controllers, we carried out simulations and experiments with parameter variation and sensor failure. As a result of simulation and experiment, it was demonstrated that the integrated CNN controllers can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the faulttolerance of the proposed CNN controllers is confirmed by comparing sliding mode control techniques.
一般社団法人日本機械学会の論文
2003-06-25