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Erschienen in: Neural Computing and Applications 3/2023

25.06.2022 | S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)

The design of an affordable fault-tolerant control system of the brushless DC motor for an active waist exoskeleton

verfasst von: Liang Yang, Chenxi Qu, Bochen Jia, Shengguan Qu

Erschienen in: Neural Computing and Applications | Ausgabe 3/2023

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Abstract

The brushless DC (BLDC) motor is a widely used method of powering various active exoskeletons such as waist exoskeleton devices. In this study, an affordable three-phase BLDC motor was designed using three 120° Hall-effect sensors to actuate an active waist exoskeleton. The fault in a Hall-effect sensor may cause the system failure. Thus, taking safety measures for the operating BLDC is a very important aspect for the device. This paper presents a model-based single-phase fault-tolerant control as a safety measure that is able to estimate speed and signal delay for Hall-effect sensors of BLDC motors used in the active waist exoskeleton. Because of motor inertia that resists changing rotational speed, the exoskeleton controller can estimate the time interval of the signal edge between the fault Hall-effect sensor and its adjacent sensor based on the sampled values of the average speed at the previous motor status. The signal delay can be used to reconstruct the faulty Hall signals. Then, the rotor position and velocity information can be corrected in time to restore the motor operation. The BLDC motor along with the controller was modeled, and a simulation was conducted to evaluate the effectiveness of the control strategy. The result showed that the fault-tolerant control could rapidly reconstruct the Hall signal required for motor rotation in the case of a single Hall-effect sensor failure, and ensured the stable operation for the BLDC motor on the exoskeleton.

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Metadaten
Titel
The design of an affordable fault-tolerant control system of the brushless DC motor for an active waist exoskeleton
verfasst von
Liang Yang
Chenxi Qu
Bochen Jia
Shengguan Qu
Publikationsdatum
25.06.2022
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-022-07362-7

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