Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model

Ghannadi, Borna; Mehrabi, Naser; Razavian, Reza Sharif; McPhee, John

doi:10.1109/iros.2017.8202200

Cited by 21 publications

(12 citation statements)

References 20 publications

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“…In this research, a planar arm model similar to the one developed by Ghannadi et al (2015) was used. The model consisted of torso, upper arm, and forearm to simulate the hand motion.…”

Section: Methodsmentioning

confidence: 99%

“…The muscle parameters of the planar arm model (i.e., insertion and origin positions, maximum isometric force, fiber optimal length, slack length, and pennation angle) were tuned to represent the dynamics of the upper extremity in the experimental condition (reaching targets in a horizontal plane elevated at the shoulder level). These parameters were tuned through a series of optimizations so that the planar model provides the same joint torques as a high-fidelity three-dimensional upper extremity model (Ghannadi et al, 2015). Kinematic and dynamic parameters of the arm model and the Hill muscle model parameters used here can be found in Tables 1, 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

“… The details of how the upper-extremity muscle groups are lumped into these representative muscles can be found in Ghannadi et al (2015) .…”

Section: Methodsmentioning

confidence: 99%

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Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Razavian

Ghannadi

et al. 2017

Front. Comput. Neurosci.

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This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.

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“…In this research, a planar arm model similar to the one developed by Ghannadi et al (2015) was used. The model consisted of torso, upper arm, and forearm to simulate the hand motion.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Razavian

Ghannadi

et al. 2017

Front. Comput. Neurosci.

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“…The exoskeleton type robot needs to be worn on the patient's body, which applies few constraints on the patient's ordinary movement. There are considerable applications of the exoskeleton type robot in the area of the rehabilitation, i.e., the upper limb exoskeleton, lower limb exoskeleton and hand exoskeleton [3][4][5]. The platform type robot is fixed on the stationary platform rather than putting on the patient's body.…”

Section: Introductionmentioning

confidence: 99%

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Cao

Cheng

Yang

et al. 2021

Sci. China Technol. Sci.

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Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. Control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system's stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient's motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

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“…Ding et al ( 2010 ) used a musculoskeletal human model (without including muscle dynamics) together with surface elecromyography (sEMG) signals to implement model-based assistance control on a rehabilitation exoskeleton . In Ghannadi et al ( 2017a ), we used a nonlinear model-predictive approach to control human-robot interaction in an upper extremity manipulandum. The method was verified using computer simulation, but experimental tests were not performed because of inefficient computation for real-time implementation.…”

Section: Introductionmentioning

confidence: 99%

Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum

2018

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Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model

Cited by 21 publications

References 20 publications

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum

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