Control Of Upper Limb Exoskeleton Research Articles

Adaptive hybrid-mode assist-as-needed control of upper limb exoskeleton for rehabilitation training

In this paper, a novel adaptive hybrid-mode assist-as-needed (AHMAAN) control algorithm is designed for the exoskeleton-assisted upper limb rehabilitation training. The overall control framework includes an outer control loop to calculate the required interaction force, and an inner control loop to drive the exoskeleton track subject’s motion, and provide specific target interaction force obtained from the outer control loop. In the outer control loop, a hybrid control mode is proposed, which consists of resistive mode and assistive mode. In this regard, a virtual tunnel is firstly established around the defined training task path, and the training mode is switched according to the deviation between the subject’s position and the boundary of the virtual tunnel. Furthermore, for tuning the strength of the resistance or assistance to the subjects with different motor capabilities, two adjustable gain factors are designed, whose values are adaptively adjusted according to the subject’s training performance by using a fuzzy logic. Then, for the inner control loop, a barrier Lyapunov function-based controller is designed to constrain exoskeleton tracking errors within the defined boundary. Meanwhile, time delay estimation (TDE) technology is used to estimate the uncertain terms of the system, and a robust adaption law is developed to compensate TDE error. Experimental tests have been performed on an upper limb exoskeleton with three healthy subjects to evaluate the effectiveness of the developed method. The results show that the proposed control scheme can be effectively applied in a variety of rehabilitation requirements and achieves better training performance than classical hybrid-mode assist-as-needed control method.

Feedback hybrid force and position control of an upper limb exoskeleton to support human movement

In the paper, a feedback hybrid control including a force feedback control and a position control is proposed to control a four degree of freedom (4-dof) upper limb exoskeleton for supporting human movement at the shoulder, elbow and wrist joints. The novelty of the paper is that it has been able to control all the interaction forces at all links in the exoskeleton robot by using the proposed control. The desired interaction forces at the links and desired position are compared with the measured interaction forces and position, respectively. Then the torque at the shoulder, the torque elbow and the torque wrist joints are controlled to compensate the force error and the position error. The gains of the proposed controller are optimized by using the Balancing Composite Motion Optimization (BCMO). The simulation and control of the 4-dof upper limb exoskeleton using the proposed control is carried out in the paper to show that the interaction forces and the position of the exoskeleton track their desired values.

Research on control method of upper limb exoskeleton based on mixed perception model

AbstractAs one of the research hotspots in the field of rehabilitation robotics, the upper limb exoskeleton robot has been widely used in the field of rehabilitation. However, the existing methods cannot comprehensively and accurately reflect the motion state of patients, which may lead to overtraining and secondary injury of patients in the process of rehabilitation training. In this paper, an upper limb exoskeleton control method based on mixed perception model of motion intention and intensity is proposed, which is based on the 6 degree-of-freedom upper limb rehabilitation exoskeleton in the laboratory. First, the kinematic information and heart rate information in the rehabilitation process of patients are collected, corresponding to patients’ motion intention and motion intensity, and fused to obtain the mixed perception vector. Second, the motion perception model based on long short-term memory neural network is established to realize the prediction of upper limb motion trajectory of patients and compared with back-propagation neural network to prove its effectiveness. Finally, the control system is built, and both offline and online test of the control method proposed are implemented. The experimental results show that the method can achieve comprehensive motion state perception of patients, realize real-time and accurate prediction trajectory according to human motion intention and intensity. The average prediction accuracy is 95.3%, and predicted joint angle error is less than 5 degrees. Therefore, the control method based on mixed perception model has good robustness and universality, which provides a new method for the active control of upper limb exoskeleton.

Force control of upper limb exoskeleton to support user movement

The dynamics analysis and control of the upper limb exoskeleton for supporting user movement is done by using the force feedforward control model. Based on a novel combination of Proportional Derivative (PD) force feedforward and Proportional Integral (PI) feedback control model [1], this paper proposes a method to assist the user’s movement for a three degree of freedom exoskeleton structure. When the user exerts force at the handle, the force is measured by a force sensor placed at the handle. These sensed forces are applied to command the actuators to support the human movement and reduce the disturbance effects of the device. This technique is well established approach in haptics for reducing the effects of inertia, damping, friction, centrifugal and Coriolis forces and gravity of haptic device [2]. Simulation result shows the torque controlled by robot to assist human for a desired loading motion.

Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton

A robust adaptive integral terminal sliding mode control strategy is proposed in this paper to deal with unknown but bounded dynamic uncertainties of a nonlinear system. This method is applied for the control of upper limb exoskeleton in order to achieve passive rehabilitation movements. Indeed, exoskeletons are in direct interaction with the human limb and even if it is possible to identify the nominal dynamics of the exoskeleton, the subject’s limb dynamics remain typically unknown and defer from a person to another. The proposed approach uses only the exoskeleton nominal model while the system upper bounds are adjusted adaptively. No prior knowledge of the exact dynamic model and upper bounds of uncertainties is required. Finite time stability and convergence are proven using Lyapunov theory. Experiments were performed with healthy subjects to evaluate the performance and the efficiency of the proposed controller in tracking trajectories that correspond to passive arm movements.

A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks

This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patient's intention is decoded by means of an active vision system, through the combination of a Kinect-based vision system, which can online robustly identify and track 3-D objects, and an eye-tracking system for objects selection. At the level of action generation, a BCI is used to control the patient's intention to move his/her own arm, on the basis of brain activity analyzed during motor imagery. The main kinematic parameters of the reaching movement (i.e., speed, acceleration, and jerk) assisted by the robot are modulated by the output of the BCI classifier so that the robot-assisted movement is performed under a continuous control of patient's brain activity. The system was experimentally evaluated in a group of three healthy volunteers and four chronic stroke patients. Experimental results show that all subjects were able to operate the exoskeleton movement by BCI with a classification error rate of 89.4±5.0% in the robot-assisted condition, with no difference of the performance observed in stroke patients compared with healthy subjects. This indicates the high potential of the proposed gaze-BCI-driven robotic assistance for neurorehabilitation of patients with motor impairments after stroke since the earliest phase of recovery.