Mass Flow Controllers (MFCs) using thermal flow sensors are widely accepted to control gas flow in the semi-conductor industry. Recently, gas flow stability and repeatability are important factors affecting device performance and yield in highly integrated semiconductor devices. Especially, Compensation of pressure disturbances at upstream of MFC is a critical issue to maintain flow stability. Therefore, pressure insensitive MFCs have been developed to overcome flow fluctuations compensation by a pressure sensor. This paper proposes a robust control method without any pressure sensor. A mathematical model is developed for MFCs to design a robust PID controller using Internal Model Control (IMC) and disturbance attenuation involving complementary pressure sensitivity. In this paper, experimental validation is performed to show the effectiveness of the proposed method.
This study addressed an advanced force display control system for a virtual surgical training simulator. In oral and orthopedics surgeries, a surgeon uses a chisel and a mallet for chiseling or cutting hard tissues such as bones and teeth. In order to construct the virtual reality training simulator for the chiseling and the cutting operations, it is necessary that the force display device as a human-machine interface has high stiffness and high responsiveness against the impact forces generated by pounding the chisel with the mallet. Therefore, we develop the force display device with ball-screw for obtain the high realistic sensation, and 2 degree-of-freedom (DOF) admittance control is applied for the moving parts of the force display device to react instantaneously. In the analysis of the force display control system using 2 DOF admittance control, the responsiveness can be enhanced by increasing the gain on high-frequency region in the feedback controller. However, to increase the gain on the high-frequency region, the force display device can be vibrated by the excitation of the sensor noise and the vibrational mode in this device. Therefore, we apply PD control using the incomplete derivative term to reduce the excessive high gain of high-frequency region in the feedback controller. And, to obtain the chiseling operation with high realistic sensation, the virtual model in the feedforward controller is applied the spring-mass-damper system with movement of supported object. The efficacy of the proposed device and control system is verified by creating the virtual experiences to operate the chisel.
Paved roads are said to be able to use them for a long time with adequate maintenances on a regular basis. However, due to a shortage of manpower for repairs, sufficient maintenance have not been taken on some municipal roads in Japan, and the deterioration has progressed rapidly. Furthermore, around 2025, many asphalt pavements paved during the period of high economic growth in Japan are expected to deteriorate rapidly, hence, a more effective method than the current manual repairing method is required. In this research, we aim to realize a machine that automatically detects and repairs cracks. As a starting point, this paper studies crack detection, one of the important elemental technologies. Assuming actual use, it is necessary to be robust to disturbances such as different colors of paved roads and illumination change due to the weather. To solve this problem, we propose a robust crack detection using deep learning. Specifically, the crack detection was performed by classifying cracked and uncracked areas by semantic segmentation using U-Net. The learning process was performed with various images including lighting changes in the training data set. As a result, we achieved a robust segmentation of cracked areas with 92.5 percent accuracy of Intersection over Union.
Case studies of artificial intelligence (AI) technologies have been reported. Applications of Deep Learning as known as one of Machine Learning technologies have expanded in some fields such an engineering, medical, and game. Back Propagation (BP) method is a basic technology of Deep Learning, is also one of optimization approaches. This paper has tried to applying the AI technology to the system control field. In enhancement of transient stability in a power system, a power system stabilizer (PSS) equipped in a generator with an automatic voltage regulator (AVR) has been useful. The PSS has some Lead Lag blocks, and has been designed by tuning of time constants in the blocks. These time constants have been determined with trial and error in a damping and synchronous torque diagrams of frequency domain by design engineers. In this paper, Back Propagation method has been applied to design of PSS. The BP method determines time constants in a Lead Lag block in PSS. This design approach realizes a controller design graphically without trial and error.