Seung Guk Baek

In the existing machine tool field, the focus was on the displacement of the feed system from the viewpoint of the motion of the machine tool. The displacement of the tool or spindle of a machine tool is useful for developing various functions. In this study, using the acceleration data of the spindle, we proposed an algorithm that tracked the displacement of the spindle with respect to the pseudo-step waveform motion. In order to solve the bandwidth problem of the pseudo-step waveform, the displacement data measured by the motor encoder of the feed system was used. In addition, in order to solve the drift problem due to double integration, a new drift removal filter was proposed and a displacement estimation algorithm was implemented. In order to examine the performance and possibility of the proposed spindle displacement estimation algorithm, it was applied to a gantry-type engraver and its excellent performance was confirmed compared to other algorithms.

In a physical human-robot interaction (pHRi) system, improving transparency which allows humans to move as if there is no robot is a challenging topic. In general physical human-robot interaction, usage of the multiaxial force sensor for robot control is the norm. However the signal measured from the force sensor contains not only the force applied to the robot by the human motion intention but also the influence of the natural force feedback between the robot and the human hand produced by the robot motion. Therefore, in order to improve the transparency, it is necessary to charcterize the dynamics of the human hand as well as the dynamics of the robot. In this paper, an algorithm to improve the transparency is proposed. The proposed algorithm uses only a multiaxial force sensor and compensates the natural force feedback by using the human hand impedance. And it further improves the transparency of the pHRi system which has a limitation adjusting admittance parameters. In order to verify the algorithm, a device that can measure the impedance of the human hand is introduced, and a system model analysis and experiments using the hydraulic upper limb exoskeleton are carried out.

In the physical human-robot interaction system, improving transparency performance which allows humans to move as if there is no robot is a challenging topic. In general physical human-robot interaction, the multiaxial force sensor is used to control the robot. However the signal measured from the force sensor involves not only the force applied to the robot by the human motion intention but also the influence of the natural force feedback between the robot and the human hand due to the motion of the robot. Therefore, in order to improve the transparency performance, it is necessary to consider the dynamics of the human hand as well as the dynamics of the robot. In this paper, an novel algorithm to improve transparency performance is proposed. The proposed algorithm uses only the multiaxial force sensor and compensates the natural force feedback by using the human hand impedance and further improves the transparency performance which has a limitation adjusting admittance parameters. The validity of the proposed algorithm is described through the system model analysis and the experiment using the hydraulic upper limb exoskeleton.

A pneumatic artificial muscle(PAM) have been studied in several fields due to its flexible property. However, it has some troubles in tracking performance and control of a PAM because of the inherent flexibility in material and air. In this study, applicability of Iterative Control(IC) algorithm to a PAM was investigated so as to achieve improvement of tracking performance despite of large non-linearity of a PAM. For this, a PAM virtual simulator was constructed, and the IC algorithm was applied to it in open loop operations and closed-loop operations. Then assessment of these tracking performance was done with time histories and tracking error rms. It seems that the IC algorithm with a linear model is valid to improvement of tracking performance of a PAM, but it has a final convergence limit on account of the use of a linear model.