Exoskeleton suit is a typical human-machine system. Control the exoskeleton suit to track the pilot’s moving trajectory as well as to minimize the human-machine interaction force. The suit will help decrease the pilot’s power consumption and assist the pilot to carry heavy load. Impedance control was introduced to the control of exoskeleton suit. As the control laws that based on the dynamic model without model uncertainty compensation will increase the human-machine force, a RBF neural network with adaptive learning algorithm was used to compensate the model uncertainty. The stability analysis of the control law was given and the simulation results show the feasibility and validity of the proposed control law.

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EXOSTATION is a project aiming at building a complete haptic control station, which allows the operator wearing an exoskeleton-based haptic interface for the human arm to remotely control a virtual slave robot. This paper briefly describes the various components : the Sensoric Arm Master (SAM), a portable haptic exoskeleton, the Exoskeleton COntroller (ECO), the slave simulator, simulating an anthropomorphic manipulator and a 3D visualisation client. Several teleoperation control strategies (impedance, hybrid control, 3-channel) have been tested and compared in order to evaluate their performances. The last has shown the best behavior in term of haptic feedback. Finally, a focus is made on the application, and how various manipulation and operation tasks can be performed to assess the system’s performances (contact wall, objects manipulation, screwing). Users who tested the system were very impressed by the easiness of operation with the exoskeleton and felt the advantages of a force feedback information.

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Many people suffer from diseases that impair muscle function, resulting in decreased hand strength and a significant reduction in hand function. The objective of this project is to design and fabricate a powered exoskeleton that assists hand function and reduces the amount of muscular force needed to pinch and grasp. This device will be portable and easily manufactured, consisting of four major sub-systems: a mechanical exoskeleton, forearm support structure, biofeedback sensor array, and motor control system. The exoskeleton will be comprised of aluminum bands incorporated into a tight fitting glove. The mechanical exoskeletonwill be actuated using braided polymer cables attached to three linear actuators via a simple pulley system that connects the most distal finger bands to the motors. The control system of the hand will incorporate a microcontroller that is coded to integrate data from the finger tip sensors to actuate the motors. A light weight, portable battery will be utilized as the power supply.

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To assist a large number of physically disabled people who no longer are in full possession of their body motion, we have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion since movement of shoulder, elbow, and wrist play a vital role in the performance of essential daily activities. The proposed ExoRob will be comprised of seven degrees of freedom to enable naturalistic movements of the human upper-limb. This paper focuses on the modeling and control of the proposed ExoRob. A kinematical model of ExoRob has been developed based on modified Denavit-Hartenberg notations. To achieve the dynamic simulation of the developed model, a nonlinear computed torque control technique is employed. In the simulation, the trajectory tracking performance of the controller is evaluated with the developed dynamic model. Simulation results show that the controller is able to drive the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are used in therapy and could be performed efficiently with the developed model and the controller. This paper also focused on the development of a 7DOF upper-limb prototype (lower scaled) master exoskeleton arm (mExoArm) which corresponds to the proposed ExoRob. The developed mExoArm will be used to maneuver the proposed ExoRob (in manual control mode) especially to provide `passive mode of rehabilitation’.

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This paper deals with the optimal design and control of an exoskeleton robot. First, the motion data from the thumb of a normal subject was captured by a vision system. As the human finger joints cannot be modeled by single revolute joints due to changing instantaneous centre of rotation, we have used 4-bar mechanisms to model each joint. Optimal 4-bars have been designed using genetic algorithms, by minimizing the error between a coupler point and points traced by the finger links. It is shown that the designed 4-bars can accurately track the motion of the human fingers. The exoskeleton is controlled by using the EMG signals obtained from the subject’s muscles. The relation between the EMG and finger motion is first learned, using a neural net. Based on the learned parameters, the subjects EMG signal is used to control a simulation of the exoskeleton joint motion.

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