For suppressing the development of diabetes mellitus and the onset of complications, an insulin therapy has been used for suppressing and normalizing the change of a blood glucose. In a blood glucose control by linear method such as conventional ARMA, however, there exists problem that results in the frequency of hypoglycemia. In a blood glucose prediction by a chaos theory, there also exists problem that results in the lower accuracy on behalf of the impossibility in the long-time prediction. For the improvement in the prediction accuracy of the blood glucose that looks like complicated time series, we propose a system combining the deterministic chaos theory using equal time interval, local fuzzy reconstruction method, and minimal linear model. By local fuzzy reconstruction method, we can predict the fasting blood glucose in the short term and then we can estimate the appropriate amount of insulin shot based on the measured bedtime blood glucose. Using the system, the change of blood glucose can be suppressed and normalized and the number of the insulin dosage a day can be reduced to once. Here we report the high effective result of applying the system to type II diabetes mellitus patient.
: This paper addresses the problem of energy-efficient power assist control for quasiperiodic motions. The simplest assist method would be to apply additional torque in proportion to the instantaneous value of torque generated by a user. In our previous study, it was shown that energy efficiency improves by flattening the torque pattern. To cope with the frequency fluctuation of our motion, we introduce a periodic disturbance observer with a frequency estimator and suppress the pulsation of the human torque based on a disturbance observer framework. The effectiveness of the proposed method is evaluated through numerical simulations and experiments with an actual electric bicycle being pedaled by a human.
In this paper, we consider controller synthesis problem of power assisting system for almost-periodic motions. The simplest assist method would be to apply assist force generated by actuators in proportion to the instantaneous value of the force generated by human. There seems to be no other choice when the motion is irregular and unpredictable. However, human persistent tasks are sometime periodic and it can be shown that such a strategy is not optimal in the sense of energy efficiency. In our previous works, an optimality condition for periodic motion is derived. This result implies that to flatten the force pattern by removing the input pulsation improve the efficiency. Thus, optimal power assisting control methods for periodic motions based on a frequency shaping were investigated. However, we observed that the pedaling frequency of human is not constant and fluctuates to some extent. In order to deal with such a situation, an adaptive compensation is introduced here. In particular, we propose a power assisting control with an adaptive notch filter to suppress the velocity pulsation. The effectiveness of the proposed assisting control method is verified through numerical simulations and experiments by using a commercial electric power assisted bicycle. Finally, the energy efficiency is evaluated by monitoring voltage drops of the battery during our experiments.
This paper proposes a method of controlling an Inverted PENdulum Type Assistant Robot (I-PENTAR) when using the attached robot arms. Coupling forces and unknown factors occur during execution of target tasks that use the arms of the I-PENTAR. Therefore precise modeling of the robot and arms is difficult. This paper presents the combination of Active Disturbance Rejection Control for compensation of unknown dynamics and feedforward input. Results regarding the control method and swinging arm tests during stabilization of I-PENTAR are presented.
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