본 연구에서는 2차자속을 파라미터로 하는 순시토크제어법에 근거한 속도센서리스 제어방식을 제안하였고, 2차자속 정보를 이용하여 운전중 변동하는 2차저항을 속도추정과 동시에 동정할 수 있음을 제시하였다. 수치시뮬레이션을 통해 얻은 결과는 다음과 같다. 1) 2차자속을 교류를 중첩한 형태로 토크응답, 속도응답과는 독립적인 제어가 가능하다. 2) 수치실험상으로 무부하시 0[rpm]을 포함하는 저속영역에서도 양호한 속도추정이 이루어졌다. 3) 위의 속도추정을 바탕으로 수[rpm]대의 저속 영역에서 양호한 속도센서리스제어가 이루어졌다. 4) 2차저항값은 제안된 동정식을 통해 실제 2차 저항값에 수렴하는 특성을 보임으로써 2차저항의 동시동정과 속도센서리스제어가 가능함을 확인하였다.

Direct teaching is an essential function for collaborative robots for easy use by non-experts. For most robots, direct teaching is implemented only in joint space because the realization of Cartesian space direct teaching, in which the orientation of the end-effector is fixed while teaching, requires a measurement of the end-effector force. Thus, it is limited to the robots that are equipped with an expensive force/torque sensor. This study presents a Cartesian space direct teaching method for torque-controlled collaborative robots without either a force/torque sensor or joint torque sensors. The force exerted to the end-effector is obtained from the external torque which is estimated by the disturbance observer-based approach with the friction model. The friction model and the estimated end-effector force were experimentally verified using the robot equipped with joint torque sensors in order to compare the proposed sensorless approach with the method using torque sensors.

This paper presents a force control based on the observer without taking any force or torque measurement from the robot which allows realizing more stable and robust human robot interaction for the developed multi-functional upper limb rehabilitation robot. The robot has four functional training modes which can be classified by the human robot interaction types: passive, active, assistive, and resistive mode. The proposed observer consists of internal disturbance observer and external force observer for distinctive performance evaluation. Since four training modes can be quantitatively identified as impedance variation, position-based impedance control with feedback and feedforward controller was applied to the assistive training mode. The results showed that the proposed sensorless observer estimated cleaner and more accurate force compared to the force sensor and the impedance controller embedded with the proposed observer completed the assistive training mode safely and properly.