In this paper, steady characteristics of the torque and current density of rotating concentric cylinder type devices using ERF with smectite particles were investigated experimentally for the purpose of designing engineering. We measured them under the various experimental factors of the dimension of the device, electric field strength, temperature, etc.. In addition, power of the device was estimated from the data of the rotating moment and the input electric power to the device. As a result, we could clarified the effect of the experimental factors on their steady characteristics.

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This study concerns with a semi-active damper, in which a pressure regulating mechanism similar to a balanced piston-type relief valve is built-in. In the previous paper, a mathematical model for the damper was derived and the dynamic characteristics of the damper was clarified. This damper can control the damping force continuously, so the limited skyhook control which is effective to suppress the vibration of the body can be applied to the semi-active suspension system. Simulation results show that the proposed damper applied the skyhook control achieves a marked improvement in the ride quality of the vehicle, surpassing the performance of passive damper.

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A hybrid vehicle with an internal combustion engine and energy storage system shows the potential for fuel-savings under the city-driving conditions, because it is possible to drive an engine near its maximum efficiency conditions and recover the energy of braking. In this paper, a hybrid vehicle with constant pressure system and Fluid Force Couple (FFC) variable displacement pump/motor is assumed. In the cases of a vehicle with an accumulator or a flywheel as the energy storage source, the energy-savings are investigated in simulation. In addition, two kinds of methods, that is, changing the driving gear ratio and the set pressure in high pressure line according to the condition of load, are proposed to improve fuel consumption. The simulated results show that fuel-savings can be realized for the hybrid vehicles with an accumulator or a flywheel as energy storage source using the proposed methods.

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This paper describes an approach for auto-tuning PID controllers using neural networks. Compared with conventional PID controllers, the realization of the proposed controller requires only measured input and output signals of a controlled object. To improve the learning convergence speed, the multiple extended Kalman algorithm (MEKA) is employed for the training of the networks system. In addition, the application of a high gain observer makes this controller capable of being operated under the measured output of a system corrupted by a strong measurement noise. The same as other self-tuning controllers, the proposed controller can tune the parameters of controller automatically. Furthermore, since the networks can provide a nonlinear control signal, this method is suitable for controlling objects containing nonlinear uncertainties, such as pneumatic cylinders, electrical motors, etc. Simulation and experimental results demonstrate the effectiveness of the proposed method for control performance and disturbance resistance.

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