Event-Triggered Adaptive Hybrid Torque-Position Control (ET-AHTPC) for Robot-Assisted Ankle Rehabilitation

Zuo, Jie; Liu, Quan; Meng, Wei; Ai, Qingsong; Xie, Sheng Quan

doi:10.1109/tie.2022.3183358

Cited by 4 publications

(4 citation statements)

References 36 publications

(60 reference statements)

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“…The compliance control approach for rehabilitation robots uses moment sensors to collect and process plantar pressure signals to obtain movement intent and improve compliance of the control system. Liu et al [15][16][17] designed a flexible ankle rehabilitation robot driven by redundant pneumatic muscles and ropes. By fixing a six-dimensional force/torque sensor between the robot pedals and the end-effector, an adaptive hybrid torque-position control scheme using event triggering was proposed to correct the robot assisted output online based on the correction index calculated from the interaction torque and tracking error.…”

Section: Ankle Active Rehabilitation Based On Intent Recognitionmentioning

confidence: 99%

Ankle active rehabilitation based on Intent recognition

Tian

Dong

et al. 2023

Preprint

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Aiming at the problem that most of the current ankle rehabilitation robots lack active patient participation, this paper proposes an intention-aware ankle active rehabilitation method. A plantar pressure sensor on the 2-SPU/RR ankle robot collects pressure data and uses an support vector machine(SVM) classifier to process the data to sense the patient's movement intention, using principal component analysis to reduce the dimensionality in order to improve the speed of classification recognition. In the first step, the rehabilitation trajectory is predefined with reference to the physician's treatment advice. In the second step, active following movement guided by movement intention is achieved based on plantar pressure, and the patient's active trajectory data is recorded. In the third step, a rehabilitation evaluation model is established through the trajectory deviation data and other indicators to objectively assess the participants' ankle rehabilitation status and provide theoretical reference for the physicians' diagnosis. The experimental results showed that the proposed ankle active rehabilitation strategy was effective, and the rehabilitation evaluation model quantitatively analysed the participants' ankle functional status based on the recorded indexes, which provided a reference for the formulation of the next step of rehabilitation training.

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Section: Ankle Active Rehabilitation Based On Intent Recognitionmentioning

confidence: 99%

Ankle active rehabilitation based on Intent recognition

Tian

Dong

et al. 2023

Preprint

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“…The ET-based control schemes have been actively explored in robotics research to lessen the communication burden. [31][32][33][34][35][36][37] Recently, an ET tracking control scheme is exploited by Abbas et al 31 to control a medical ultrasound robot. Wang et al 32 presented an ET-based sliding mode control to track the pre-specified trajectory using a lower-extremity exoskeleton robot.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Llorente-Vidrio et al 33 combined the Electromyography-based ET sliding mode control with the deep differential neural network for a lower limb exoskeleton device. Zuo et al 37 proposed the hybrid torque-position control scheme for ankle rehabilitation while implementing the event-triggered mechanism to reduce the control updates.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the problem of limited communication in the framework of admittance control has not been addressed explicitily. [16][17][18][19][20] On the other hand, although there has been a few works on event-triggered control for rehabilitation robots [32,33,37], most of them have focused on lower extremity exercises. Therefore, for the first time, the ETAB admittance approach is proposed for the robot-assisted upper limb rehabilitation problem under the dynamic uncertainties, limited communications and interaction between subject and robot.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event-triggered adaptive control for upper-extremity therapeutic robot in active-assist mode: A simulation study

Abbas,

Narayan,

Dwivedy

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Upper extremity rehabilitation exercises are essential for individuals recovering from injuries or conditions that affect their arm and hand functionality. Robot-assisted therapy has gained popularity as it offers precise control, objective assessment, and customizable treatment programs. However, several challenges persist, including uncertainties in the patient’s and robot dynamics, limited communication, and the need to maintain a compliant patient-robot interaction. Therefore, an event-triggered adaptive backstepping (ETAB) admittance control strategy is proposed in this work to address these challenges. Initially, the framework of the robot-assisted therapeutic process is briefly explained. The architecture of the proposed control strategy is formulated with two control modules. Thereafter, the adaptive backstepping technique is employed with an online adaptation law to deal with dynamic uncertainties. Moreover, the problem of limited communication is handled using the proposed design of a Lyapunov-based event-triggered mechanism. The admittance controller is integrated to maintain a compliant patient-robot interaction and consider the participation of the patient in the therapeutic sessions. The effectiveness of the proposed control strategy is verified using an end-effector type rehabilitation robot performing two different rehabilitation exercises. Furthermore, a comparative performance analysis is carried out with the proportional-integral-derivative controller (PID) and the adaptive sliding mode controller (ASMC). Based on the simulation runs, the proposed controller has shown promising tracking behavior, appropriate compliant interaction, and considerable reduction in the transmitted signals during the passive and active-assist training exercises.

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