A Gait Trajectory Control Scheme Through Successive Approximation Based on Radial Basis Function Neural Networks for the Lower Limb Exoskeleton Robot

Ren, Bin; Luo, Xurong; Wang, Yao; Chen, Jiayu

doi:10.1115/1.4046937

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…A gait trajectory is a powerful tool for LLE control as a mechanical system needs to predict the gait before its operation based on gait patterns. By extracting features of recorded gaits, gait trajectory can be approximated and predicted with machine learning algorithms (Ren, Luo, et al., 2020). Gait trajectory can also be used for planning and optimizing the LLE operation (Ren, Shang, et al., 2020); however, these methods usually take a long computational time and are complicated in terms of practical implementation for the controllers.…”

Section: Methodsmentioning

confidence: 99%

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Ren¹,

Luo²,

et al. 2021

Computer aided Civil Eng

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Lower limb exoskeletons (LLEs) are sets of mechanical devices used to support the action of human lower limbs. This recently developed technology has unprecedented potential in the construction industry by increasing the strength, endurance, and other physical capabilities of construction workers. For safety considerations, LLEs need reliable and responsive controllers to closely match their mechanical operation with human gait in a synchronous manner. This research proposes the use of physical human–robot interactive (pHRI) controllers that are suitable for construction tasks. The proposed pHRI integrates a gait trajectory‐based musculoskeletal model with iterative control algorithms. To minimize the trajectory tracking error between LLEs and human lower limbs, the gait dynamic was modeled as a spring damping and impedance model for supporting and swing phases. An iterative adaptive controller was developed for trajectory tracking and predication. To validate the proposed model, an in‐lab experiment to simulate typical construction activities was conducted, allowing us to assess tracking error. The experiment results suggest that the proposed model can minimize the trajectory tracking error to a level acceptable for safe operation. The iterative controllers allow fast error convergence for different construction scenarios with proper calibration. Therefore, the proposed pHRI iterative controllers are reliable and suitable for complicated activities within the dynamic working conditions intrinsic to construction sites.

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Section: Methodsmentioning

confidence: 99%

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Ren¹,

Luo²,

et al. 2021

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the performance of the RBF compensation controller is still determined by the upper bound of external disturbances, and large disturbances may affect tracking accuracy or even cause system unstable. Ren et al [27] used RBF neural network to estimate exoskeleton dynamic parameters and used the gradient descent method to sequentially solve the optimal neural network parameters. Duong et al [28] and Chen et al [29] used RBF neural network and designed adaptive update laws to compensate for model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

Han,

Ma,

Wang

et al. 2024

Chin. J. Mech. Eng.

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The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings. Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm. The Radial Basis Function (RBF) neural network is used widely to compensate for modeling errors. In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability, a neural network robust control algorithm based on computed torque method is proposed in this paper, focusing on trajectory tracking. It innovatively incorporates the robust adaptive term while introducing the RBF neural network term, improving the compensation ability for modeling errors. The stability of the algorithm is proved by Lyapunov method, and the effectiveness of the robust adaptive term is verified by the simulation. Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out, and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°, respectively. The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

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Adaptive RBF neural network-computed torque control for a pediatric gait exoskeleton system: an experimental study

Narayan,

Abbas,

Patel

et al. 2023

Intel Serv Robotics

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A Gait Trajectory Control Scheme Through Successive Approximation Based on Radial Basis Function Neural Networks for the Lower Limb Exoskeleton Robot

Cited by 7 publications

References 24 publications

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

Adaptive RBF neural network-computed torque control for a pediatric gait exoskeleton system: an experimental study

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