IKEDA AtsutoshiDepartment of Energy and Materials Associate Professor |
Fictitious Reference Iterative Tuning (FRIT) have been reported for many applications, in which the control gain can be optimized so that the closed loop system corresponds with the reference model based on only one experiment data. However, when the reference model is not appropriate, there is a problem that not only the control performance is deteriorated but also the control system may become unstable. Instead of using model matching to the closed-loop reference model, we thought that this problem could be solved by optimizing the settling time and overshoot. To measure settling time and overshoot, the time response is required. The time response can be predicted using Kaneko's method [Takahashi, 2019], but this method requires the order of the closed loop transfer function. In this paper, the method [Takahashi, 2019] is reconsidered as a frequency response based method so that the order is not required. Furthermore, as another approach of FRIT, this paper proposes a method called 'Virtual Time-response based Iterative Gain Evaluation and Redesign' (V-Tiger) which iterates to measure the overshoot and settling time from the virtual time responses, and to evaluate and redesign the controller gain.
Accurate measurement and evaluation of air-sea momentum is one of the basic technologies for the prediction of climate change. Although calculation of sea surface stress requires high-speed measurement of wind speed at sea, the influence of anemometer fluctuation on measurement accuracy has not been sufficiently evaluated. In this research, we propose a method to perform high-precision motion correction by attaching multiple IMU sensors to an anemometer, and perform quantitative evaluation of the motion correction effect in indoor experiments using a robot arm. In the proposed method, we assume that the instantaneous motion of the anemometer is on a spherical surface to reduce the numerical integration error of the acceleration data. In addition, the effects of measurement errors are reduced by integrating measurement values using two IMU sensors. Experiments are conducted in a windless room, and the proposed method shows that the influence of motion falls within the nominal accuracy of the anemometer.