Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Xu, Guoqing; Song, Aiguo; Li, Huijun

doi:10.1007/s10846-010-9462-3

Cited by 73 publications

(43 citation statements)

References 29 publications

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“…According to [18,19] and our previous experimental investigation [15,20], a mass-damper-spring relationship between the position and the force is established for the rehabilitation robot by impedance control, which is represented as…”

Section: Control Methodsmentioning

confidence: 99%

Robot-assisted humanized passive rehabilitation training based on online assessment and regulation

Pan

Song

Duan

et al. 2015

BME

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Abstract. Robot-assisted rehabilitation has been developed and proved effective for motion function recovery. Humanization is one of the crucial issues in the designing of robot-based rehabilitation system. However, most of the previous investigations focus on the simplex position control when comes to the control system design of robot-assisted passive training, and pay little attention to the dynamic adjustment according to the patient's performances. This paper presents a novel method to design the passive training system using a developed assessing-and-regulating section to online assess the subject's performances. The motion regulating mechanism is designed to dynamically adjust the training range and motion speed according to the actual performances, which is helpful to improve the humanization of the rehabilitation training. Moreover, position-based impedance control is adopted to achieve compliant trajectory tracking movement. Experimental results demonstrate that the proposed method presents good performances not only in motion control but also in humanization.

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Section: Control Methodsmentioning

confidence: 99%

Robot-assisted humanized passive rehabilitation training based on online assessment and regulation

Pan

Song

Duan

et al. 2015

BME

Self Cite

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“…Impedance control has triggered off a number of developments for interaction control. These include, for instance, a Lyapunov-based approach (Mendoza et al, 2012), model predictive impedance control (Falaki and Towhidkhah, 2012), model-free impedance control or intelligent control like neural networks (Dehghani et al, 2010;Modares et al, 2016), fuzzy logic (Deneve et al, 2008;Akdogan and Adli, 2011;Ju et al, 2005), or a combination of both (Kiguchi et al, 2007;Xu et al, 2011), as well as stiffness control in the task-space as in the work of Chávez-Olivares et al (2015), where some case studies of different stiffness regulators are proposed and the corresponding Lyapunov's stability analysis is presented.…”

Section: Introductionmentioning

confidence: 99%

Saturating Stiffness Control of Robot Manipulators with Bounded Inputs

Rodríguez-Liñán

Mendoza

Bonilla

et al. 2017

International Journal of Applied Mathematics and Computer Science

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A saturating stiffness control scheme for robot manipulators with bounded torque inputs is proposed. The control law is assumed to be a PD-type controller, and the corresponding Lyapunov stability analysis of the closed-loop equilibrium point is presented. The interaction between the robot manipulator and the environment is modeled as spring-like contact forces. The proper behavior of the closed-loop system is validated using a three degree-of-freedom robotic arm.

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“…Choi et al 4 expanded on this research and presented a two-dimensional leg. Other researchers who studied on control algorithms have used a sliding mode control based on a nonlinear disturbance observer, 5 an adaptive impedance controller based on an evolutionary dynamic fuzzy neural network, 6 and fractional fuzzy adaptive sliding mode control. 7 Others have used adaptive self-organizing fuzzy sliding mode control, 8 gain scheduling neural multipleinput multiple-output (MIMO) dynamic neural proportional-integral-derivative (DNN PID) control, 9 hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID control, 10 proxy-based sliding mode control, 11 and twisting and super twisting sliding control.…”

Section: Introductionmentioning

confidence: 99%

“…J h is used to depict the relationship between input and output and is a matrix, so we call it structure matrix. Equation (6) can also be expressed as…”

mentioning

confidence: 99%

Analysis and control of a parallel lower limb based on pneumatic artificial muscles

Jiang

Tao

2017

Advances in Mechanical Engineering

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Most robots that are actuated by antagonistic pneumatic artificial muscles are controlled by various control algorithms that cannot adequately imitate the actual muscle distribution of human limbs. Other robots in which the distribution of pneumatic artificial muscle is similar to that of human limbs can only analyze the position of the robot using perceptual data instead of rational knowledge. In order to better imitate the movement of a human limb, the article proposes a humanoid lower limb in the form of a parallel mechanism where muscle is unevenly distributed. Next, the kinematic and dynamic movements of bionic hip joint are analyzed, where the joint movement is controlled by an observer-based fuzzy adaptive control algorithm as a whole rather than each individual pneumatic artificial muscle and parameters that are optimized by a neural network. Finally, experimental results are provided to confirm the effectiveness of the proposed method. We also document the role of muscle in trajectory tracking for the piriformis and musculi obturator internus in isobaric processes.

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Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Cited by 73 publications

References 29 publications

Robot-assisted humanized passive rehabilitation training based on online assessment and regulation

Robot-assisted humanized passive rehabilitation training based on online assessment and regulation

Saturating Stiffness Control of Robot Manipulators with Bounded Inputs

Analysis and control of a parallel lower limb based on pneumatic artificial muscles

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