2自由度制御系における制御対象の変動を考慮した フィードバック制御器の低次元化2022 · <p>In this paper, a new feed-back controller reduction scheme is proposed. The two-degree-of-freedom(2DOF) control system has a feed-forward controller and a feed-back controller, thereby expanding the range of feasible transfer characteristics. However, this increases the overall order of the controller. It is necessary to reduce the order in the implementation of the controller. In 2DOF control systems, it is known that the transmission characteristics change when the plant changes. A method using the Riccati equation is employed to reduce the order of the feed-back controller preserving the transfer characteristics. A numerical example is illustrated to show the effectiveness of the method.</p>
ホール素子変位センサを利用した教育用磁気浮上システムの開発：カルマンフィルタによる電流センサレス電圧駆動制御システムの構築2019 · <p>In this study, we developed a current sensorless voltage driven magnetic levitation system with a Hall element displacement sensor using Kalman filter. The system costs less and has higher portability than the current driven system developed in a previous study, and is utilized effectively for model-based development education. However, the use of the displacement sensor for a voltage driven magnetic levitation system causes two problems, namely compensation for measurement error and correction of Hall element output voltages in the sensor. Therefore, we have proposed compensation and correction methods using Kalman filter. To verify the validity of the proposed methods, a magnetic levitation control experiment with the developed system was conducted.</p>
科学技術教育のための磁気浮上システムの開発:—ニューラルネットワークを利用したホール素子変位センサによる磁気浮上制御—2016 · <p>In this study, we developed a magnetic levitation system using a Hall element displacement sensor with neural network for science and technology education. The sensor configured with three Hall elements was devised in order to measure displacement from an electromagnet to a levitated object with a permanent magnet. Use of the Hall element displacement sensor achieves a lower-cost magnetic levitation system. Furthermore, three-layered feedforward neural network was utilized in order to improve the precision of the Hall element displacement sensor. Finally, operation verification of the developed magnetic levitation system was conducted by designing state feedback regulator with observer.</p>
