In this paper, we propose a design scheme for the intelligent control for a pneumatic servo system. In this design scheme, the unknown parameters are identified first and then the plant model is determined whether to be minimum-phase or not, because the discrete-time plant model of the pneumatic servo system becomes a minimum phase or a non-minimum phase depending on the difference in the sampling period and the load mass. Next a model reference adaptive control system (MRACS) is adopted in the case of minimum-phase and an adaptive pole-assignment control system (APACS) is used in the other case. Furthermore, in this design scheme, neural networks (NN) in the adaptive controller and a deference process of the signals in the identification mechanism are introduced to eliminate the influence of non-linear elements caused by compressibility of the air and various frictions. The effectiveness of the proposed design scheme is confirmed by experiments using an existing pneumatic servo system.

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In this paper, a new type of displacement sensor with pneumatic feedback system is proposed. The piston of the sensor can follow a measured object automatically by a pneumatic servomechanism with a nozzle-flapper as a sensor. For precise measurement, the piston is supported by an air bearing and has very low friction. The gap between the nozzle and the measured object is kept fixed, so that the displacement of the measured object can be detected by measuring the displacement of the piston by an optical linear scale attached to it. As a result, this sensor can realize noncontacting displacement measurements and has a much longer measuring range compared with the traditional air micrometer.
The optimal parameters of the sensor structure are obtained by means of simulation and experiment. When the accuracy of the sensor is verified using a micrometer, the repeatability error of the sensor is below ±0.5μm and the linearity error of senor is below ±2μm with a measuring range beyond 10mm. It is concluded that this sensor has sufficient accuracy, with the resolution of linear scale to be 0.5μm.

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Rehabilitation robots have become increasingly necessary in Japan due to the countries aging population. Pneumatic power is effective in this field because of its flexibility. In this study, a pneumatic robot arm to help subjects move from a sitting to a standing position using a pneumatic cylinder and a servo valve and its effectiveness were evaluated quantitatively. The stiffness control was applied for its movement. The performance of the robot was examined by measuring the movement of the human’s body using a digital video camera. The robot was used to help several subjects. This was done to investigate the effect of differences in physique. The knee torque was calculated to evaluate the assistance ratio. It became clear that the stiffness control is effective, but the control parameter must be changed owing to the weight of the subjects.

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This paper deals with an extension of the frequency-shaped sliding mode control with pre-filter (FS-SMC-PF), which was originally proposed by Ito and Nonami and partly modified by Yanada and Ohnishi, to model reference control. In this paper, it is revealed that the FS-SMC-PF scheme has an undesirable nature in that the break frequency of the pre-filter automatically shifts from an assigned value to a value determined by the eigenvalues assigned to the sliding mode dynamics and a plant parameter. A reference model is introduced to remove the undesirable effect that the nature influences on the control performance. The model reference FS-SMC-PF scheme is applied to an electrohydraulic position control system. The effects of the break frequencies of two lowpass filters used for FS-SMC-PF on the control performance are investigated. In addition, the robustness of the scheme is examined under various conditions and compared with that of a usual SMC and an LQ-based robust control using a disturbance observer. It is shown that the model reference FS-SMC-PF scheme possesses very high robustness to modeling error, parametric variation and steady disturbance.

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