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환자와 로봇의 모델 불확도를 고려한 상지재활로봇의 채터링 없는 슬라이딩 모드 제어
압둘 마난 칸(Abdul Manan Khan),윤덕원(Deok-Won Yun),한창수(Changsoo Han) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.5
Need to develop human body’s posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess human motor function and generate the command to assist in compliance with complex human motion. Upper limb rehabilitation robots, are one of those robots. These robots are used for the rehabilitation of patients having movement disorder due to spinal or brain injuries. One aspect that must be fulfilled by these robots, is to cope with uncertainties due to different patients, without significantly degrading the performance. In this paper, we propose chattering free sliding mode control technique for this purpose. This control technique is not only able to handle matched uncertainties due to different patients but also for unmatched as well. Using this technique, patients feel active assistance as they deviate from the desired trajectory. Proposed methodology is implemented on seven degrees of freedom (DOF) upper limb rehabilitation robot. In this robot, shoulder and elbow joints are powered by electric motors while rest of the joints are kept passive. Due to these active joints, robot is able to move in sagittal plane only while abduction and adduction motion in shoulder joint is kept passive. Exoskeleton performance is evaluated experimentally by a neurologically intact subjects while varying the mass properties. Results show effectiveness of proposed control methodology for the given scenario even having 20 % uncertain parameters in system modeling.
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Estimation of Desired Motion Intention and Compliance Control for Upper Limb Assist Exoskeleton
압둘 마난 칸,윤덕원,Khalil Muhammad Zuhaib,Junaid Iqbal,Rui-Jun Yan,Fatima Khan,한창수 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.2
In this paper, we have addressed two issues for upper limb assist exoskeleton. 1) Estimation of DesiredMotion Intention (DMI); 2) Robust compliance control. To estimate DMI, we have employed Extreme LearningMachine Algorithm. This algorithm is free from traditional Neural Network based problems such as local minima,selection of suitable parameters, slow convergence of adaptation law and over-fitting. These problems cause lot ofproblem in tuning the intelligent algorithm for the desired results. Furthermore, to track the estimated trajectory, wehave developed model reference based adaptive impedance control algorithm. This control algorithm is based onstable poles of desired impedance model, forcing the over all system to act as per desired impedance model. It alsoconsiders robot and human model uncertainties. To highlight the effectiveness of the proposed control algorithm, wehave compared it with simple impedance and target reference based impedance control algorithms. Experimentalevaluation is carried on seven degree of freedom upper limb assist exoskeleton. Results describe the effectiveness ofELM algorithm for DMI estimation and robust tracking of the estimated trajectory by the proposed model referenceadaptive impedance control law.
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Passivity Based Adaptive Control for Upper Extremity Assist Exoskeleton
한창수,압둘 마난 칸,윤덕원,Mian Ashfaq Ali,Khalil Muhammad Zuhaib,원조,Junaid Iqbal,신규식 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.1
Upper limb assist exoskeleton robot requires quantitative techniques to assess human motor function andgenerate command signal for robots to act in compliance with human motion. To asses human motor function,we present Desired Motion Intention (DMI) estimation algorithm using Muscle Circumference Sensor (MCS) andload cells. Here, MCS measures human elbow joint torque using human arm kinematics, biceps/triceps musclemodel and physiological cross sectional area of these muscles whereas load cells play a compensatory role for thetorque generated by shoulder muscles as these cells measure desire of shoulder muscles to move the arm and notthe internal activity of shoulder muscles. Furthermore, damped least square algorithm is used to estimate DesiredMotion Intention (DMI) from these torques. To track this estimated DMI, we have used passivity based adaptivecontrol algorithm. This control techniques is particular useful to adapt modeling error of assist exoskeleton robotfor different subjects. Proposed methodology is experimentally evaluated on seven degree of freedom upper limbassist exoskeleton. Results show that DMI is well estimated and tracked for assistance by the proposed controlalgorithm.
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