リザバーコンピューティングを用いたモデル予測制御によるHEVパワートレインのアクティブ制振制御

抄録

In general, model predictive control (MPC) requires to solve the optimization problem within the control period. To overcome this limitation, we had proposed a novel MPC method based on the prediction of disturbances using an echo state network (ESN) in active vibration control of hybrid electric vehicle (HEV) powertrains. To achieve real-time control in this method, ESN predicts the future disturbances and applies them to MPC, instead of completing the real-time optimization within the control period. However, the prediction accuracy of ESN will decrease when patterns are learned additionally in the previous method and will bring worse control performance. To avoid this weak point, we developed a new learning method of ESN with clustering technic by self-organizing map (SOM) in the proposed method. The performance of the proposed method is verified through the simulations under the different combustion conditions of the internal combustion engine (ICE).