Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Chi, Haozhen; Li, Xuefang; Liang, Wenyu; Cao, Jianting; Ren, Qinyuan

doi:10.3389/frobt.2019.00113

Cited by 11 publications

(2 citation statements)

References 48 publications

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“…In the process of modeling the single actuator model, a simplified spring-dashpot model mentioned in [12] is employed to describe the single-dimensional physical properties of PAM because of the similarity with biological muscle in viscoelasticity. The modeling method here is similar to our previous work [34,35]. This method has been verified to be a feasible solution, and this work aims to verify that this kind of modeling method has generality through the modeling work of different objects whose properties are similar to biological muscles.…”

Section: Knowledge-guided Modelingmentioning

confidence: 89%

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Chi

Liang

et al. 2021

Actuators

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Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Due to the compliance and continuous deformation capacity, rehabilitation devices driven by soft actuators are attached to widespread attention. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. Finally, several simulations and experiments are carried out to evaluate the performance of the developed system, and the results show that the developed system with the proposed controller can achieve expected control performance under various operations.

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Section: Knowledge-guided Modelingmentioning

confidence: 89%

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Chi

Liang

et al. 2021

Actuators

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“…The ILC technique can gradually improve the tracking performance of a system by repeating the same movement [25] and has been used to control several soft actuators with satisfactory results [26][27][28]. The ILC iteratively generates the input for the FRSBA and learns its deformability simultaneously.…”

Section: Iterative Learning Controller (Ilc)mentioning

confidence: 99%

Individual deformability compensation of soft hydraulic actuators through iterative learning-based neural network

et al. 2021

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Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the difference in the individual deformability occurring in manual fabrication of soft actuators. Furthermore, soft actuators used in wearable rehabilitation devices involve a large response delay which hinders the application of such devices for at-home rehabilitation. In this paper, a feed-forward control method for soft actuators with a large response delay, comprising a simple feed-forward neural network (FNN) and an iterative learning controller (ILC), is proposed. The proposed method facilitates the effective learning and acquisition of the inverse model (i.e. the model that can generate control input to the soft actuator from a target trajectory) of soft actuators. First, the ILC controls a soft actuator and iteratively learns the actuator deformability. Subsequently, the FNN is trained to obtain the inverse model of the soft actuator. The control results of the ILC are used as training datasets for supervised learning of the FNN to ensure that it can efficiently acquire the inverse model of the soft actuator, including the deformability and the response delay. Experiments with fiber-reinforced soft bending hydraulic actuators are conducted to evaluate the proposed method. The results show that the ILC can learn and compensate for the actuator deformability. Moreover, the iterative learning-based FNN serves to achieve a precise tracking performance on various generalized trajectories. These facts suggest that the proposed method can contribute to the development of robotic rehabilitation devices with soft actuators and the field of soft robotics.

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Model-free Adaptive Control of Dielectric Elastomer Actuator

Mao,

Zhang,

et al. 2023

Intelligent Robotics and Applications

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Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot

Cited by 11 publications

References 48 publications

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Control of a Rehabilitation Robotic Device Driven by Antagonistic Soft Actuators

Individual deformability compensation of soft hydraulic actuators through iterative learning-based neural network

Model-free Adaptive Control of Dielectric Elastomer Actuator

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