Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Jin, Xinglai; Zhu, Shiqiang; Zhu, Xingyi; Zhang, Xuequn

doi:10.1177/1687814016686665

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…Then, to improve the control precision and robustness of the system, the discrete sliding mode control law and prior torque are introduced. In [23] , an improved single-input fuzzy sliding mode controller together with an adaptive switching controller is proposed to promote the tracking performance of the system. Because the system is affected by interference, the rejection control [24] is regarded to overcome the active interference and applied it to track the human gait trajectory, in that the extended state observer is designed to estimate the total uncertain disturbances.…”

Section: Introductionmentioning

confidence: 99%

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Wang

Song

2019

Applied Sciences

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Aiming at a pneumatic artificial muscle (PAM) lower extremity exoskeleton, a control mechanism based on hybrid phase sliding mode control (SMC) is proposed. First of all, the human gait cycle is mainly divided into the swing phase and stance phase, and the lower extremity exoskeleton phase models are established by the Euler–Lagrange method, respectively. Secondly, the lower limb exoskeleton is inevitably affected in the diverse working environment, and the exoskeleton model has nonlinear and strong coupling characteristics, which both increase the control difficulty. In this situations, a robust sliding mode control method is designed based on an Extended State Observer (ESO). Thirdly, the pneumatic muscle takes time to contract and relax, and then the joint input torque cannot jump when the gait phase changes, hence, the smoothing switching of the assistive control scheme is introduced to solve it. The smoothing switching time is detected by a phase detector, and the phase detector is designed by the plantar pressure information. Finally the comparative simulation shows that this control strategy has the advantages of fast time, high control precision and no jump during control torque switching. Pneumatic artificial muscle contraction rate curve shows that the pneumatic muscles’ motion range meets the control requirement of the exoskeleton.

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Section: Introductionmentioning

confidence: 99%

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Wang

Song

2019

Applied Sciences

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“…The device would adjust the gait trajectory online according to the indications from a human-machine interaction force set-up. In [19] a humanrobot interaction controller was introduced for a lower extremity exoskeleton whose aim was to improve tracking performance with the development of a fuzzy SMC that considered system uncertainties. This way the controller was able to drive the exoskeleton to shadow the wearer in the presence of weaker interactive driving forces .…”

Section: Literature Reviewmentioning

confidence: 99%

Human–robot collaboration for safe object transportation using force feedback

Solanes

Gracia

Muñoz-Benavent

et al. 2018

Robotics and Autonomous Systems

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This work presents an approach based on multi-task, non-conventional sliding mode control and admittance control for human-robot collaboration aimed at handling applications using force feedback. The proposed robot controller is based on three tasks with different priority levels in order to cooperatively perform the safe transportation of an object with a human operator. In particular, a high-priority task is developed using non-conventional sliding mode control to guarantee safe reference parameters imposed by the task, e.g., keeping a load at a desired orientation (to prevent spill out in the case of liquids, or to reduce undue stresses that may compromise fragile items). Moreover, a second task based on a hybrid admittance control algorithm is used for the human operator to guide the robot by means of a force sensor located at the robot tool. Finally, a third low-priority task is considered for redundant robots in order to use the remaining degrees of freedom of the robot to achieve a pre-set secondary goal (e.g., singularity avoidance, remaining close to a homing configuration for increased safety, etc.) by means of the gradient projection method. The main advantages of the proposed method are robustness and low computational cost. The applicability and effectiveness of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.

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“…SMC has two specific features of disturbance rejection and insensibility to uncertainties by designing the sliding mode surface [11]. Different types of SMC strategies are employed for robotic exoskeletons, such as terminal SMC [12], nonsingular terminal SMC [13] and fuzzy SMC [14]. Nevertheless, the performance of sliding mode control is susceptible to the existence of a chattering phenomenon, which may increase control effort and excite high-frequency oscillation [15].…”

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based Nonlinear Terminal Sliding Mode Control With Feedforward Compensation for Lower Extremity Exoskeleton

Long

Peng²

2022

IEEE Access

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This paper presents and experimentally demonstrates an extended state observer (ESO)based nonlinear terminal sliding mode control strategy with feedforward compensation (ESO-F-NTSMC) for lower extremity exoskeleton. Since the lower extremity exoskeleton (LEE) is a coupled humanexoskeleton coordination system, the internal or external disturbances and uncertainties affect its performance. A nonlinear terminal sliding mode control with feedforward compensation (F-NTSMC) is proposed to drive the lower extremity to shadow the target human gait trajectory. An ESO is employed to estimate the total disturbances including these caused by the chattering phenomenon in F-NTSMC. ESO-F-NTSMC can assure that the human gait trajectory tracking can converge to a bounded region smoothly and robustly. The phase identification-based human gait generation approach is also presented. The derivation process of the ESO-F-NTSMC is shown the and the Lyapunov-based stability analysis is conducted. To illustrate the proposed method's effectiveness, experiments are performed on three human subjects walking on the floor at a natural speed. The results demonstrate that the exoskeleton can actively collaborate with the user under the proposed method.

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Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Cited by 18 publications

References 37 publications

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Human–robot collaboration for safe object transportation using force feedback

Extended State Observer-Based Nonlinear Terminal Sliding Mode Control With Feedforward Compensation for Lower Extremity Exoskeleton

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