A novel adaptive control for reaching movements of an anthropomorphic arm driven by pneumatic artificial muscles

Wang, Ting; Chen, Xiuxiang; Qin, Wen

doi:10.1016/j.asoc.2019.105623

Cited by 18 publications

(15 citation statements)

References 21 publications

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“…The EMG signals amplified by 5000 gain 20 with a sampling rate of 1 kHz. The recording duration was 15 seconds.…”

Section: Experimental Setup and Emg Signal Processing To Obtain Musclmentioning

confidence: 99%

“…19 Some research to understanding the function of the neuromuscular system has used in anthropomorphic systems. 20 In this paper, a reaching movement by a two-link arm with 6 fuzzy-genetic muscles has investigated. The authors previously showed that Zajac's muscle model is unable to take account of planar movement details during the experiment because of few tuning parameters.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method

Rahatabad

2020

Int Clin Neurosci J

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Background: The central nervous system (CNS) is optimizing arm movements to reduce some kind of cost function. Simulating parts of the nervous system is one way of obtaining accurate information about the neurological and treatment of neuromuscular diseases. The primary purpose of this paper is to model and control the human arm in a reaching movement based on reinforcement learning (RL) theory. Methods: First, Zajac’s muscle model has improved by a fuzzy system. Second, the proposed muscle model applied to the 6 muscles, which are responsible for a two-link arm that moves in the horizontal plane. Third, the model parameters are approximated based on the genetic algorithm (GA). Experimental data recorded from healthy subjects for assessing the approach. At last, the RL algorithm has utilized to guide the arm for reaching tasks. Results: The results show that: (1) The proposed system is temporally similar to a real arm movement. (2) The RL algorithm can generate the motor commands obtained from electromyographies (EMGs). (3) The similarity of obtained activation function from the system has compared with the real data activation function, which may prove the possibility of RL in the CNS (basal ganglia). Finally, in order to have a graphical and effective representation of the arm model, the virtual reality environment of MATLAB has been used. Conclusion: Since the RL method is a representative of the brain’s control function, it has some features, such as better settling time, not having any peek overshoot, and robustness.

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“…The EMG signals amplified by 5000 gain 20 with a sampling rate of 1 kHz. The recording duration was 15 seconds.…”

Section: Experimental Setup and Emg Signal Processing To Obtain Musclmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method

Rahatabad

2020

Int Clin Neurosci J

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“…Pneumatic artificial muscles have been adopted in some rehabilitation robots. Model-based (Huang et al, 2018 ; Wang et al, 2019 ) and model-free methods (Tu et al, 2017 ; Giancarlo, 2019 ) have been proposed to tackle nonlinear and hysteretic characteristics. Considering the difficulties in PAM modeling, model-free methods have been proved to be more advantageous.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

Liu

et al. 2021

Front. Neurorobot.

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The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow–wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness.

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“…Wang et al, in [ 9 ], proposed an adaptive fuzzy backstepping controller for motion control of a two-link anthropomorphic arm, which is subjected to frictions and model uncertainties. The PAM-actuated manipulator has been applied in upper-limb rehabilitation training.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Humaidi

Ibraheem

Azar

et al. 2020

Entropy

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

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A novel adaptive control for reaching movements of an anthropomorphic arm driven by pneumatic artificial muscles

Cited by 18 publications

References 21 publications

Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method

Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

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