In the first report, a sound operated directional control valve was proposed in order to widen the application of pneumatic drives to a multi-degree-of-freedom wearable system. In this paper a main valve is proposed which can be driven by a small pilot pressure change to compose a sound operated directional control valve. First, the working principle of the main valve and the method to decide the parameters of it are explained. Next, the characteristics of the sound operated directional control valve which consists of a pilot valve and a main valve are investigated experimentally. Furthermore, preliminary experiments are conducted which use two sound operated directional control valves to control multi-actuator. Finally, a multi-degree-of-freedom wearable pneumatic system is constructed by using a Dry-Ice Power Cell as the portable gas supply, wearable actuators developed in a previous study, and two sound operated directional control valves developed in this paper. Experimental results show that the sound operated directional control valve is feasible and practical.

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A pneumatic servo seems to be effectively applied to many varieties of gripping or compressing work, because its high compressibility is useful to the force control. In a force control system, the characteristic of the object is comprised in a closed loop, so its influence has to be considered in the design of the controller. However, it is generally unknown and often changes during operation. In particular, the characteristics of the plant changes significantly, when the object is moving. To cope with these problems, more intelligent control schemes are required. In this paper, we propose a design scheme of a model reference adaptive control for a force control system with pneumatic servo. The effectiveness of the proposed design scheme is confirmed by experiments using an existent pneumatic servo system.

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The purpose of this study is to improve performance of hydraulic servo systems using a state feedback Neural Network and observer. It is generally not easy to acquire high control performance in hydraulic servo systems, since they have an intense non-linearity. The state feedback Neural Network compensator is one of the available options to get over this inherent problem. However, to measure the entire state variables of the actual control object is generally difficult. Using an observer is an effective method for estimating missing state variables of the control object. In this paper, a state feedback Neural Network control system with observer is proposed for improving the control performance of hydraulic servo systems under the condition of reduced state variables. At first, two Neural Network control systems with observer are designed, and they are incorporated into the hydraulic motor system. Then, the angular velocity and position control performance of the two control systems are experimentally compared to determine the optimum structure of the control system. From the experimental result, suitable structure of the Neural Network with observer is confirmed. Finally, it is clarified that the proposed Neural Network control system has excellent performance.

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