Abdul Manan Khan

Adaptive impedance control for upper limb assist exoskeleton

Need to develop human bodys posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we present a new technique for the upper limb power exoskeleton robot in which load is gripped by the human subject and not by the robot while the robot assists. Main challenge is to find non-biological signal based human desired motion intention to assist as needed. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the force sensors is used to estimate the human interactive force from which desired human motion is extracted using adaptive Radial Basis Function Neural Network (RBFNN). Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five DOF are passive while two are active. Active joints include shoulder and elbow in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joints. To ensure high quality performance model reference based adaptive impedance controller is employed. Exoskeleton performance is evaluated experimentally by a neurologically intact subject which validates the effectiveness.

Static & dynamic sliding mode control of ball and beam system

This paper presents an application of static and dynamic sliding mode control of ball and beam system. Conventional PID controllers use incomplete simplified models and are mostly designed for linear systems. Although, some PID controllers are designed for nonlinear system but they are using simplified incomplete model which do not cater for matched disturbances. Our proposed control laws using static & dynamic sliding mode control use complete nonlinear system without model approximation. Moreover, static sliding mode control (SSMC) caters for matched disturbance rejection as well. There is an inherent issue of chattering with static sliding mode control (SSMC). However, dynamics sliding mode control (DSMC) counter it well. DSMC is not only equally effective when it comes to matched disturbance rejection but also removes chattering as well. In the end detailed comparative analysis is presented and experimental results confirm the superiority of DSMC.

Study on Efficacy of Gait Training for Hemiplegia Patients Using Lower-Limb Wearable Robot

1 한양대학교 메카트로닉스공학과 (Department of Mechatronics Engineering, Hanyang University) 2 한양대학교 기계공학과 (Department of Mechanical Engineering, Hanyang University) 3 한양대학교 기계설계공학과 (Department of Mechanical Design Engineering, Hanyang University) 4 고영테크놀러지 제어기술부 (Control Technology Department, Koh Young Technology) 5 한양대학교 의학과 (Department of Medicine, Hanyang University) 6 한성대학교 기계시스템공학과 (Department of Mechanical System Engineering, Hansung University) 7 한양대학교 로봇공학과 (Department of Robot Engineering, Hanyang University) Corresponding author: cshan@hanyang.ac.kr, Tel: +82-31-400-5247

Upper extremity assist exoskeleton robot

Need to develop human bodys posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess motor function and generate the command for the robots to act accordingly with complex human structure. In this paper, we focus on developing new technique for the upper limb power exoskeleton in which load is handled by the human subject and not by the robot. Main challenge along with the design complexity is to find the desired human motion intention and to develop an algorithm to assist as needed accordingly. For this purpose, we used newly developed Muscle Circumference Sensor (MCS) instead of electromyogram (EMG) sensors. MCS together with the load cells is used to estimate the desired human intention by which desired trajectory is generated. The desired trajectory is then tracked by passivity based adaptive control technique. Developed Upper limb power exoskeleton has seven degrees of freedom (DOF) in which five are passive and two are active. Active joints include shoulder and elbow, powered by electric motors and move in Sagittal plane while abduction and adduction motion in shoulder joint is provided by the passive joint. Performance of the exoskeleton is evaluated experimentally by a neurologically intact subject. The results show that after adjusting the motion intention recognition algorithm for the subject, the robot assisted effectively and the subject only felt nominal load regardless of the weight in hand.

A novel method for 3D reconstruction: Division and merging of overlapping B-spline surfaces ☆

Abstract B-spline surfaces, extracted from scanned sensor data, are usually required to represent objects in inspection, surveying technology, metrology and reverse engineering tasks. In order to express a large object with a satisfactory accuracy, multiple scans, which generally lead to overlapping patches, are always needed due to, inter-alia, practical limitations and accuracy of measurements, uncertainties in measurement devices, calibration problems as well as skills of the experimenter. In this paper, we propose an action sequence consisting of division and merging. While the former divides a B-spline surface into many patches with corresponding scanned data, the latter merges the scanned data and its overlapping B-spline surface patch. Firstly, all possible overlapping cases of two B-spline surfaces are enumerated and analyzed from a view of the locations of the projection points of four corners of one surface in the interior of its overlapping surface. Next, the general division and merging methods are developed to deal with all overlapping cases, and a simulated example is used to illustrate aforementioned detailed procedures. In the sequel, two scans obtained from a three-dimensional laser scanner are simulated to express a large house with B-spline surfaces. The simulation results show the efficiency and efficacy of the proposed method. In this whole process, storage space of data points is not increased with new obtained overlapping scans, and none of the overlapping points are discarded which increases the representation accuracy. We believe the proposed method has a number of potential applications in the representation and expression of large objects with three-dimensional laser scanner data.

Estimation of desired motion intention using extreme learning machine for upper limb assist exoskeleton

One of the complicated issue in compliance control for rehabilitation and assistive robots is to predict right humans motion intention. In this paper, we have proposed an algorithm to estimate Desired Motion Intention (DMI) so that better compliance could be provided by the rehabilitation and assitive robots. Proposed algorithms is based on Extreme Learning Machine (ELM) and takes inputs from different sensors. These sensors provide information about current angular position, speed and the force applied by the human on robot. Proposed algorithm is free from the issues which appear in traditional Radial Basis Function Neural Network (RBFNN) such as local minima, selection of suitable parameters, slow convergence of adaptation law and over-fitting. These issues cause many problems for such algorithm in tuning for each different individual and makes it impractical for our application. Developed algorithm is experimentally evaluated for two kinds of trajectories which are employed for the Activities of Daily Living (ADL) for rehabilitation purposes. These trajectories include motion in line, circle and mixture of both (line and circular). Experimental results describe the successfully implementation of proposed algorithm in prediction/estimation of the Desired Motion Intention (DMI).

Muscle circumference sensor and model reference-based adaptive impedance control for upper limb assist exoskeleton robot

Graphical Abstract In this paper, we have addressed two issues for upper limb assist exoskeleton: (1) estimation of human desired motion intention (DMI) using non-biological-based sensors; and (2) compliant control using model reference-based adaptive approach. For non-biological-based DMI estimation, we have employed Muscle Circumference Sensor (MCS) and load cells. MCS measures human elbow joint torque using human arm kinematics, biceps/triceps muscle model, and physiological cross-sectional area of these muscles. So, using MCS, we have measured Biceps/Triceps internal muscle activity and we have tried to reduce it by providing robotic assistance. To extract DMI, we have employed radial basis function neural network (RBFNN). RBFNN uses position, velocity, and human force to estimate DMI which is further tracked by the impedance control law. This algorithm is based on model reference-based adaptive impedance control law which drives the overall assist exoskeleton to the desired reference impedance model, giving required compliance. To highlight the effectiveness, we have compared proposed control algorithm with simple impedance and adaptive impedance control algorithms. Experimental results demonstrate the reduced muscle activity and active compliance for subject wearing the robot.

Passivity Based Adaptive Control and Its Optimization for Upper Limb Assist Exoskeleton Robot

The need for human body posture robots has led researchers to develop dexterous design of exoskeleton robots. Quantitative techniques to assess human motor function and generate commands for robots were required to be developed. In this paper, we present a passivity based adaptive control algorithm for upper limb assist exoskeleton. The proposed algorithm can adapt to different subject parameters and provide efficient response against the biomechanical variations caused by subject variations. Furthermore, we have employed the Particle Swarm Optimization technique to tune the controller gains. Efficacy of the proposed algorithm method is experimentally demonstrated using a seven degree of freedom upper limb assist exoskeleton robot. The proposed algorithm was found to estimate the desired motion and assist accordingly. This algorithm in conjunction with an upper limb assist exoskeleton robot may be very useful for elderly people to perform daily tasks.

Chattering Free Sliding Mode Control of Upper-limb Rehabilitation Robot with Handling Subject and Model Uncertainties

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.