Kyeong Ha Lee

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.

Three-stage servo-valves are popularly used in hydraulic systems that require large flow rate and high pressure. For a proper control of flow direction and flow rate fed into a hydraulic actuator, securing a proper position control bandwidth is a critical task for the servo-valve. In this paper, a set of popular control methods are systematically studied and a control method is selected. It is proven that the feedforward model-inverse control is the most effective method in terms of the control bandwidth. In the present work, the feedforward closed-loop architecture is adopted and the closed-loop system is estimated in a linear discrete-time transfer function by recursive least squares method. On recognizing a nonminimum phase zero problem, this work implements the zero magnitude error tracking control, an approximate model-inverse technique, in order to overcome the problem. As a result, the effectiveness of the proposed feedforward model-inverse position control strategy is verified.

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.

Many researchers have been studying a walking robot that can walk in an Unknown environment. But there is a problem. When the leg is landing, the motor or torque sensor is damaged by the amount of impact generated from the uncleaned ground. In order to solve this problem, many researchers have tried to reduce the amount of impulse by placing a passive compliance element in a motor-pack.The efforts have been made to reduce the impact by creating virtual m, c, and k elements through active compliance, impedance control or admittance control. In this paper, we designed a new concept motor-pack that enters the joint of a walking robot. The main function is to make the motor-pack into a package type to protect important components from external shocks and speed up the communication speed due to the circuit intensiveness. The motorpack consists of motor, gear, bearing, encoder and torque sensor. In addition, this paper validates the SEA module as a torque sensor.

In this paper, we propose lumped methods of flexible elements of Flexible-Link manipulator to Flexible-Joint, and compare their performance evaluation to suggest the most suitable modeling method. The first method is to obtain the Equivalent Stiffness through the static analysis of Flexible-Link. The second method is to obtain the natural frequency through the modal analysis of the Flexible-Link System and to derive the Equivalent Stiffness. The final method is to construct an FRF (Frequency Response Function) Model using the input and output signals of the Flexible-Link System and to find Equivalent stiffness through approximation using polynomial and exponential functions. Flexible-Joint and Rigid-Link manipulator is modeled based on Equivalent Stiffness obtained by the above methods, and compared with the existing Flexible-Link model.

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.