Integral admittance shaping: A unified framework for active exoskeleton control

Nagarajan, Umashankar; Aguirre-Ollinger, Gabriel; Goswami, Ambarish

doi:10.1016/j.robot.2015.09.015

Cited by 82 publications

(52 citation statements)

References 52 publications

(85 reference statements)

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“…Then, the ideal dynamics of the human-robot system are given as follows [19]: where ℎ , ℎ , ℎ are, respectively, the moment of inertia, joint damping coefficient, and joint stiffness coefficient of the human leg; ℎ ( ) is the hip-joint angle; ℎ ( ) is the net muscle torque acting on the joint. , , are, respectively, the moment of inertia, joint damping coefficient, and joint stiffness coefficient of the exoskeleton; ( ) is the exoskeleton joint angle; ( ) is the actuator torque.…”

Section: System Modeling and Trajectory Generationmentioning

confidence: 99%

“…All the parameters are cited in [19]. The parameters, just for calculating, are obtained from real experiment and useful for controller simulation.…”

Section: Remarkmentioning

confidence: 99%

“…For instance, a sensitivity amplification control is proposed in [19] which could track the desired trajectory by minimizing the interaction torque. In [20], a robust sliding mode controller is proposed to guarantee the stability in disturbance situation, and the boundary layer is introduced to reduce the chattering problem of sliding model control.…”

Section: Introductionmentioning

confidence: 99%

“…Human body is a part of the system obviously. Additional, the system has high-order features when taking the human body as a passive part [19]. Hence, the control strategies above are not available because of the "explosion of complexity" [23].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the model built in [19], this paper converts the transfer to the state space form and introduces uncertainties and disturbances which are more actual and complicated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Command Filter Backstepping Sliding Model Control for Lower‐Limb Exoskeleton

Yang

Zhang

Wang

et al. 2017

Mathematical Problems in Engineering

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A command filter adaptive fuzzy backstepping control strategy is proposed for lower-limb assisting exoskeleton. Firstly, the humanrobot model is established by taking the human body as a passive part, and a coupling torque is introduced to describe the interaction between the exoskeleton and human leg. Then, Vicon motion capture system is employed to obtain the reference trajectory. For the purpose of obviating the "explosion of complexity" in conventional backstepping, a second-order command filter is introduced into the sliding mode control strategy. The fuzzy logic systems (FLSs) are also applied to handle with the chattering problem by estimating the uncertainties and disturbances. Furthermore, the stability of the closed-loop system is proved based on the Lyapunov theory. Finally, simulation results are presented to illustrate the effectiveness of the control strategy.

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Section: System Modeling and Trajectory Generationmentioning

confidence: 99%

“…All the parameters are cited in [19]. The parameters, just for calculating, are obtained from real experiment and useful for controller simulation.…”

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared with the model built in [19], this paper converts the transfer to the state space form and introduces uncertainties and disturbances which are more actual and complicated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Command Filter Backstepping Sliding Model Control for Lower‐Limb Exoskeleton

Yang

Zhang

Wang

et al. 2017

Mathematical Problems in Engineering

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A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

Hsiao,

2023

Asian Journal of Control

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This paper proposes an admittance control algorithm for lower‐limb exoskeletons to assist able‐bodied wearers in performing energy‐efficient stance‐squat transitions while carrying a payload of up to 20 kg. A two‐degree‐of‐freedom (DOF) model dedicated to the stance‐squat transition is derived, which takes into account the wearer's torso inclination angle and the vertical displacement of the hip joint. The torso inclination angle is obtained from an inertial measurement unit (IMU) located at the backpack of the exoskeleton. In addition, the moment caused by the torso plus the payload with respect to the hip joint is estimated. Then a model‐based disturbance observer (DOB) is used to estimate the wearer's joint torque as an indication of the motion intention, and the exoskeleton complies with the intention by following the desired joint angular velocity generated from the estimated wearer's joint torque through an admittance function. Experiments with different admittance parameters are conducted, and the energy consumption of the wearer is evaluated in terms of the normalized energy consumption index (NECI) and the physiological cost index (PCI). Furthermore, experimental comparisons of energy consumption are made for a subject carrying a payload of up to 20 kg and performing repeated stance‐squat transitions with and without the exoskeleton. The results show that a 50% reduction of energy consumption (in terms of PCI) is achieved by the exoskeleton with the proposed admittance control strategy.

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Human‐centered active torque‐based AAN impedance control for lower limb patient‐exoskeleton coupling system in the rehabilitation

Wang,

Guo,

Tao

et al. 2023

Intl J Robust & Nonlinear

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This article presents a human‐centered active torque‐based assist‐as‐needed (ATAAN) impedance control for lower limb patient‐exoskeleton coupling system (LLPECS), which considers the coupling effects (e.g., elastic connections, human soft tissues). The proposed double loop structure controller is composed of the establishment of the ATAAN impedance model in the outer‐loop and an adaptive interaction torque‐based controller in the inner‐loop. In the outer‐loop, the patient's muscle torque is estimated by a nonlinear disturbance observer, and the estimated result is used to approximate the patient's active torque. The parameters of ATAAN impedance model are modulated according to the approximation of active torque and the patient's position error to achieve the assist‐as‐needed property. In the inner‐loop, the desired interaction torque, which is required to realize the target ATAAN impedance model, is designed by a robust computed torque controller. The desired trajectory of the exoskeleton is planned online according to the desired interaction torque with the dynamics model of coupling effects. The prescribed performance controller is utilized to achieve accurate trajectory tracking of the exoskeleton, which further enables the interaction torque to track the desired value. The stability of the system and realization of the target ATAAN impedance model are rigorously proved by the Lyapunov method. Co‐simulation experiments are conducted to demonstrate the superiority of the proposed method compared with other existing controllers and its robustness against modeling uncertainties. The effectiveness of the proposed method has been verified through experiments in rehabilitation scenarios.

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Integral admittance shaping: A unified framework for active exoskeleton control

Cited by 82 publications

References 52 publications

Command Filter Backstepping Sliding Model Control for Lower‐Limb Exoskeleton

Command Filter Backstepping Sliding Model Control for Lower‐Limb Exoskeleton

A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

Human‐centered active torque‐based AAN impedance control for lower limb patient‐exoskeleton coupling system in the rehabilitation

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