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Jun Kobayashi 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.5
In this study, the effect of exploiting an oscillatory motion of a manipulator on large force generation is demonstrated using simulations. Firstly, a natural frequency approximation method is devised, and a preferable posture of the manipulator is selected based on the approximation. The preferable posture is a posture in which the natural frequency of the manipulator is low. Since, in general, high frequency vibration is considered to be an undesirable phenomenon for mechanical systems, the manipulator should be operated in the preferable posture. Secondly, a method to oscillate the manipulator is proposed, and its performance is investigated using simulations. The method capitalizes on the oscillatory motion of the manipulator for efficient large force generation. Specifically, the method uses Van der Pol (VDP) oscillator to exploit an oscillatory motion of the manipulator. A force reference signal, which is a command to make the manipulator oscillate, is produced by the VDP oscillator. Due to the entrainment property of the VDP oscillator, the force reference signal can synchronize with the motion of the manipulator. The efficient large force generation is attained by the synchronization. Thirdly, a force control system that enables you to obtain the desired amount of force is designed based on the force generation method. By adjusting the natural frequency of the VDP oscillator, the purpose of the force control system is realized. Finally, a theoretical proof of the entrainment property of the VDP oscillator coupled with a linear mechanical system is established with an averaging method, and simulations exemplify the validity of the proof.
Stabilizing Model Predictive Control of Hybrid Systems with Discrete Dynamics
Koichi Kobayashi,Jun-ichi Imura,Kunihiko Hiraishi 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper discusses the stabilization problem of hybrid systems using control Lyapunov functions(CLFs). ACLF approach is one of the powerful method sin thest abilization of nonlinear systems. However, there are a few results for the stabilization of hybrid systems. In this paper, to develop stability theory of hybrid systems with discrete dynamics, a construction method of a CLF is proposed. Furthermore, based on the obtained CLF, a stabilizing model predictive control algorithm is alsoproposed. Finally, the effectiveness of the proposed method is shown by a numerical example.
Observability Analysis of Boolean Networks with Biological Applications
Koichi Kobayashi,Jun-ichi Imura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper discusses the observability analys is of Boolean networks. A Boolean network model is one of the typical models of biological networks such as gene regulatory networks and metabolic networks, and it is one of the significant to picsto consider the analysis and control problems using Boolean network models. In this paper, after the notion of the observability is defined, we derive a necessary and sufficient condition for the system to be observable. Furthermore, we show an example of a neurotransmitter signaling pathway.
Takeshi Kobayashi,Toshiaki Imanishi,Taizo Hanai,Ichiro Aoyagi,Jun Uemura,Katsuhiro Araki,Hiroshi Yoshimoto,Takeshi Harima,Hiroyuki Honda 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.