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論文
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神谷, 好承 ; 関, 啓明 ; 疋津, 正利 ; 酒井, 史敏
概要:
DC servo motors that are made as manufactured goods in the factory are widely used as actuators for driving many automatic machines. Then the manufactured driver (amplifier) that is matched to its servo motor is coveniently chosen to
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drive when aiming at high performance of the motion control. Motion of motor that is driven by the manufactured servo driver has very complicated dynamic characteristics. In this study, it is tried to make clear about inner composition of the servo driver through measuring the dynamic characteristics of the servo motor which is driven by the manufactured servo driver. And it is tried to express mathemati-cally transfer function of the velocity control system.
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論文
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張, 勤 ; 神谷, 好承 ; 関, 啓明 ; 疋津, 正利
概要:
NC (numerical control) techniques for driving the X-Y axes servo motors are widely used for the industrial machine tools such as lathe, milling machine, and so on. General NC techniques have not been able to control the angular
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displacement or angular velocity of the X-Y axes servo motors in the real time of motion. Therefore, it is impossible to reduce a residual vibration by use of the sensor feedback of the spring-mass load when the spring-mass load is driven by the NC controller. Here, in this study the new NC control method that can reduce the residual vibration by use of the sensor feedback of the load in the real time of motion is proposed. The new NC control method that is proposed in this study contains a V-F transformer that translates the analogue signal to the series of pulses in the loop of feedback, so the control system that is proposed in this study becomes nonlinear. Also the design method that can construct the system of real time sensor feedback in the new NC controller is verified.
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論文
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関, 啓明 ; 佐々木, 健 ; 高野, 政晴
概要:
In this paper, a method for detecting kinematic constraints in a plane when the shapes of the grasped object and the env
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ironment are not given is presented.It is one of the important function for intelligent tasks of home, maintenance, assembly and disassembly robots. This method utilizes the displacement and force information obtained by“active search motion”of a robot. In reality, this information includes some uncertainties such as friction, slack and elasticity. A new neural network configuration for this detection is proposed. It consists of two multilayer networks (<I>primary and secondary network</I>) . The primary network learns the movable space (constraint) obtained by the search motion. By the generated link weights which reflect the movable space, the secondary network determines the type and the orientation of the constraint. Simulation and experimental results are presented and analyzed.
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