Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton

Long, Yi; Du, Zhijiang; Lin, Cong; Wang, Weidong; Zhang, Zhiming; Dong, Wei

doi:10.1016/j.isatra.2017.01.006

Cited by 102 publications

(66 citation statements)

References 29 publications

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“…In this paper, an n ‐degree of freedom (DOF) model of the lower‐limb exoskeleton is considered. Thus, the dynamic equation of the swing leg of exoskeleton can be described as follows :

M false(q false) \overset{q}{true} + C false(q, \overset{q}{true} false) \overset{q}{true} + G false(q false) + f false(q, \overset{q}{true} false) = τ + τ_{h} + τ_{d}

where q ∈ℜ n joint variable vector, M ( q )∈ℜ n × n is the inertia matrix,

C false(q, \overset{q}{true} false) \in ℜ^{n \times n}

is the coriolis and centripetal matrix, G ( q ) ∈ ℜ n is a vector of gravitational forces,

f false(q, \overset{q}{true} false) \in ℜ^{n}

is the friction forces, τ d ∈ ℜ n is the vector of unknown external disturbances, τ ∈ ℜ n and τ h ∈ ℜ n are the vectors of exoskeleton and human torque, respectively.…”

Section: Dynamics Of Lower‐limb Exoskeletonmentioning

confidence: 99%

“…The cyclical gait period is 2 seconds; however, it can be made periodic by repeating signals. Hence, we can compute the appropriate expression as follows :

\begin{matrix} q_{h i p} = q_{d 1} = 3.85 c o s false(0.33 t + 2.14 false) + 71.6 c o s false(3.49 t - \\ 1.88 false) + 41 c o s false(4.68 t - 0.3 false) and \\ q_{k n e e} = q_{d 2} = 40.9 c o s false(1.04 t - 0.208 false) + \\ 157 c o s false(5.82 t - 0.047 false) + 82.3 c o s false(7.49 t - 4.13 false) \end{matrix}

where q d 1 and q d 2 are the desired angular position of hip and knee joints, respectively. And the parameters of human and exoskeleton are given in Table .…”

Section: Numerical Simulationsmentioning

confidence: 99%

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Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

Ahmed

Wang

Tian

2018

Asian Journal of Control

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This paper introduces a robust adaptive fractional-order non-singular fast terminal sliding mode control (RFO-TSM) for a lower-limb exoskeleton system subject to unknown external disturbances and uncertainties. The referred RFO-TSM is developed in consideration of the advantages of fractional-order and non-singular fast terminal sliding mode control (FONTSM): fractional-order is used to obtain good tracking performance, while the non-singular fast TSM is employed to achieve fast finite-time convergence, non-singularity and reducing chattering phenomenon in control input. In particular, an adaptive scheme is formulated with FONTSM to deal with uncertain dynamics of exoskeleton under unknown external disturbances, which makes the system robust. Moreover, an asymptotical stability analysis of the closed-loop system is validated by Lyapunov proposition, which guarantees the sliding condition. Lastly, the efficacy of the proposed method is verified through numerical simulations in comparison with advanced and classical methods.

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“…In this paper, an n ‐degree of freedom (DOF) model of the lower‐limb exoskeleton is considered. Thus, the dynamic equation of the swing leg of exoskeleton can be described as follows :

M false(q false) \overset{q}{true} + C false(q, \overset{q}{true} false) \overset{q}{true} + G false(q false) + f false(q, \overset{q}{true} false) = τ + τ_{h} + τ_{d}

where q ∈ℜ n joint variable vector, M ( q )∈ℜ n × n is the inertia matrix,

C false(q, \overset{q}{true} false) \in ℜ^{n \times n}

is the coriolis and centripetal matrix, G ( q ) ∈ ℜ n is a vector of gravitational forces,

f false(q, \overset{q}{true} false) \in ℜ^{n}

is the friction forces, τ d ∈ ℜ n is the vector of unknown external disturbances, τ ∈ ℜ n and τ h ∈ ℜ n are the vectors of exoskeleton and human torque, respectively.…”

Section: Dynamics Of Lower‐limb Exoskeletonmentioning

confidence: 99%

“…The cyclical gait period is 2 seconds; however, it can be made periodic by repeating signals. Hence, we can compute the appropriate expression as follows :

\begin{matrix} q_{h i p} = q_{d 1} = 3.85 c o s false(0.33 t + 2.14 false) + 71.6 c o s false(3.49 t - \\ 1.88 false) + 41 c o s false(4.68 t - 0.3 false) and \\ q_{k n e e} = q_{d 2} = 40.9 c o s false(1.04 t - 0.208 false) + \\ 157 c o s false(5.82 t - 0.047 false) + 82.3 c o s false(7.49 t - 4.13 false) \end{matrix}

where q d 1 and q d 2 are the desired angular position of hip and knee joints, respectively. And the parameters of human and exoskeleton are given in Table .…”

Section: Numerical Simulationsmentioning

confidence: 99%

Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

Ahmed

Wang

Tian

2018

Asian Journal of Control

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“…The high-level control scheme is calculated in the embedded PC, and the driven commands are sent to the actuator through the Copley driver. For more details, we refer to our previous work in [30]. Figure 8.…”

Section: Experiments Setting and Model Learningmentioning

confidence: 99%

Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton

Wang

Dong

et al. 2018

Applied Sciences

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To achieve ideal force control of a functional autonomous exoskeleton, sensitivity amplification control is widely used in human strength augmentation applications. The original sensitivity amplification control aims to increase the closed-loop control system sensitivity based on positive feedback without any sensors between the pilot and the exoskeleton. Thus, the measurement system can be greatly simplified. Nevertheless, the controller lacks the ability to reject disturbance and has little robustness to the variation of the parameters. Consequently, a relatively precise dynamic model of the exoskeleton system is desired. Moreover, the human-robot interaction (HRI) cannot be interpreted merely as a particular part of the driven torque quantitatively. Therefore, a novel control methodology, so-called probabilistic sensitivity amplification control, is presented in this paper. The innovation of the proposed control algorithm is two-fold: distributed hidden-state identification based on sensor observations and evolving learning of sensitivity factors for the purpose of dealing with the variational HRI. Compared to the other state-of-the-art algorithms, we verify the feasibility of the probabilistic sensitivity amplification control with several experiments, i.e., distributed identification model learning and walking with a human subject. The experimental result shows potential application feasibility.

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“…The LLRE robot can directly interact with patients' lower limbs [18,19]. On one hand, LLRE needs to provide power for lower limbs to follow the standard gait trajectory [20]. On the other hand, it is necessary to ensure that the power provided can be adaptively adjusted according to the patient's active muscle force to ensure active participation [21].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Zhu

Song

et al. 2020

Applied Sciences

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As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer's active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer's leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient.

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Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton

Cited by 102 publications

References 29 publications

Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

Robust Adaptive Fractional‐Order Terminal Sliding Mode Control for Lower‐Limb Exoskeleton

Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

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