Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) With Enhanced Training Safety

Zhang, Mingming; Xie, Shane; Li, Xiaolong; Zhu, Guoli; Meng, Wei; Huang, Xiaolin; Veale, Allan Joshua

doi:10.1109/tie.2017.2733425

Cited by 93 publications

(70 citation statements)

References 30 publications

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“…The human-robot interaction force transforms into the modification of the desired trajectory to track the patient's motion intention. For example, Zhang et al 23 developed a compliant ankle rehabilitation robot (CARR) actuated by four Festo Fluidic muscles (FFM) with admittance controller in task space. The adaptation law is proposed to change the admittance parameters based on real-time ankle posture and interaction torque.…”

Section: Introductionmentioning

confidence: 99%

Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

Guo

Han

et al. 2019

International Journal of Advanced Robotic Systems

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A human-robot interactive control is proposed to govern the assistance provided by a lower limb exoskeleton robot to patients in the gait rehabilitation training. The rehabilitation training robot with two lower limb exoskeletons is driven by the pneumatic proportional servo system and has two rotational degrees of freedom of each lower limb. An adaptive admittance model is adopted considering its suitability for human-robot interaction. The adaptive law of the admittance parameters is designed with Sigmoid function and the reinforcement learning algorithm. Individualized admittance parameters suitable for patients are obtained by reinforcement learning. Experiments in passive and active rehabilitation training modes were carried out to verify the proposed control method. The passive rehabilitation training experimental results verify the effectiveness of the inner-loop position control strategy, which can meet the demands of gait tracking accuracy in rehabilitation training. The active rehabilitation training experimental results demonstrate that the personal adaption and active compliance are provided by the interactive controller in the robot-assistance for patients. The combined effects of flexibility of pneumatic actuators and compliance provided by the controller contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation.

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Section: Introductionmentioning

confidence: 99%

Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

Guo

Han

et al. 2019

International Journal of Advanced Robotic Systems

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“…I N recent years, robots have been widely used in medicine [1]- [3], aerospace [4], [5] and marine vessel [6], [7], etc. As the complexity of the task and the demand of control accuracy increase, single robot hardly meet the mission requirement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

Kong

Yang

et al. 2019

IEEE Trans. Cybern.

237

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In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. Firstly, a FNN learning algorithm is developed to identify the unknown plant model. Secondly, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Thirdly, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, Integral Barrier Lyapunov function (IBLF) is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, Some simulation examples are carried out to verify the effectiveness of the designed control.

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“…The robot consists of two parallel platforms, a fixed platform and a moving platform that is actuated by four PMs in parallel. An updated version with more powerful Festo muscles and three-link lower platform to regulate the ankle three DoFs were further developed and tested [20,21]. This design allows the participant's lower limb and shinbone to stay stationary during the ankle training process.…”

Section: Introductionmentioning

confidence: 99%

Design and Modelling of a Compliant Ankle Rehabilitation Robot Redundantly Driven by Pneumatic Muscles

Meng

Zhu

Zuo

et al. 2019

2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) With Enhanced Training Safety

Cited by 93 publications

References 30 publications

Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

Design and Modelling of a Compliant Ankle Rehabilitation Robot Redundantly Driven by Pneumatic Muscles

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