Model reference adaptive impedance control of rehabilitation robots in operational space

SummaryIn this paper, a novel robust model reference adaptive impedance control (RMRAIC) scheme is presented for an active transtibial ankle prosthesis. The controller makes the closed loop dynamics of the prosthesis similar to a reference impedance model and provides asymptotic tracking of the response trajectory of this impedance model. The interactions between human and prosthesis are taken into account by designing a second-order reference impedance model. The proposed controller is robust against parametric uncertainties in the nonlinear dynamic model of the prosthesis. Also, the controller has robustness against bounded uncertainties due to unavailable ground reaction forces and unmeasurable feedbacks of accelerations at the socket place. Moreover, an appropriate Series Elastic Actuator (SEA) mechanism for the prosthetic ankle is included in this work and its effects are discussed. Tracking performance and stability of the closed-loop system are proven via the Lyapunov stability analysis. Using simulations on an overall amputee prosthetic foot system, the effectiveness of the proposed RMRAIC controller is investigated for the task of level ground walking.

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Model reference adaptive impedance control in Cartesian coordinates for physical human–robot interaction

Advanced Robotics ◽

10.1080/01691864.2014.933125 ◽

2014 ◽

Vol 28 (19) ◽

pp. 1277-1290 ◽

Cited By ~ 26

Author(s):

Mojtaba Sharifi ◽

Saeed Behzadipour ◽

G.R. Vossoughi

Keyword(s):

Impedance Control ◽

Human Robot Interaction ◽

Robot Interaction ◽

Model Reference ◽

Cartesian Coordinates ◽

Adaptive Impedance Control ◽

Model Reference Adaptive

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A model reference adaptive variable impedance control method for robot

MATEC Web of Conferences ◽

10.1051/matecconf/202133603005 ◽

2021 ◽

Vol 336 ◽

pp. 03005

Author(s):

Xinchao Sun ◽

Lianyu Zhao ◽

Zhenzhong Liu

Keyword(s):

Real Time ◽

Tracking Control ◽

Control Method ◽

Impedance Control ◽

Tracking Error ◽

Reference Trajectory ◽

Control Parameters ◽

Model Reference ◽

Force Tracking ◽

Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

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Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot interaction

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/09544119211054919 ◽

2021 ◽

pp. 095441192110549

Author(s):

Javad Omrani ◽

Majid M Moghaddam

Keyword(s):

Time Delay ◽

Impedance Control ◽

Adaptive Controller ◽

Time Delay Estimation ◽

Human Robot Interaction ◽

Delay Estimation ◽

Tracking Errors ◽

Model Reference ◽

Dynamic Relationship ◽

Model Reference Adaptive

A nonlinear Time Delay Estimation (TDE) based model reference adaptive impedance controller was developed for Tarbiat Modares University Upper Limbs Rehabilitation Robot (TUERR). The proposed controller uses a stable reference impedance model, which produces desired dynamic relationship between applied force and position error for the robot End-effector to track the desired trajectory. TDE based model reference adaptive controller estimates unknown system dynamics and uncertainties, and the adaption law modifies the controller gains. Using a Lyapunov function was shown trajectory tracking errors in the overall system are bounded. In addition, a performance-based velocity profile proposed to modify the pace of trajectory planning considering the deviation from the desired path. Finally, the performance of the presented controller and rehabilitation process is experimentally investigated for TUERR.

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Adaptive Impedance Control for a Tendon-Sheath-Driven Compliant Gripper

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.532.74 ◽

2014 ◽

Vol 532 ◽

pp. 74-77

Author(s):

Kai Wang ◽

Xing Song Wang

Keyword(s):

Impedance Control ◽

Mathematic Model ◽

Tendon Sheath ◽

Strain Gages ◽

Control Scheme ◽

Adaptive Impedance Control ◽

Adaptive Control Strategy ◽

Gripping Force ◽

Model Reference Adaptive ◽

Soft Control

This paper investigates the feasibility of adaptive impedance control scheme for compliant gripper. A compliant gripper was designed for manipulation tasks requiring precision position and force control. The gripper is actuated by tendon-sheath transmission system and use strain gages to measure both the displacement and gripping force. Position based impedance control is used to control the contact force to made the gripper more compliantly. Due to the nonlinear of the structure; it is difficult to establish the mathematic model and kinematical equations. Therefore, combine model reference adaptive control strategy with impedance control to realize the soft control of the compliant gripper.

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sEMG-based variable impedance control of lower-limb rehabilitation robot using wavelet neural network and model reference adaptive control

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-10-2019-0210 ◽

2020 ◽

Vol 47 (3) ◽

pp. 349-358 ◽

Cited By ~ 1

Author(s):

Rohollah Hasanzadeh Fereydooni ◽

Hassan Siahkali ◽

Heidar Ali Shayanfar ◽

Amir Houshang Mazinan

Keyword(s):

Closed Loop ◽

Reference Trajectory ◽

Rehabilitation Robot ◽

Model Reference ◽

Content Type ◽

Loop Control ◽

Rehabilitation Robots ◽

Limb Rehabilitation ◽

Model Reference Adaptive ◽

Lower Limb Rehabilitation

Purpose This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots. Design/methodology/approach Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory. Findings The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field. Originality/value The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.

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