{"title":"人体关节力矩的无传感器估计用于下肢外骨骼辅助步态康复的鲁棒跟踪控制","authors":"A. Abdullahi, R. Chaichaowarat","doi":"10.3390/jsan12040053","DOIUrl":null,"url":null,"abstract":"Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Sensorless Estimation of Human Joint Torque for Robust Tracking Control of Lower-Limb Exoskeleton Assistive Gait Rehabilitation\",\"authors\":\"A. Abdullahi, R. Chaichaowarat\",\"doi\":\"10.3390/jsan12040053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training.\",\"PeriodicalId\":37584,\"journal\":{\"name\":\"Journal of Sensor and Actuator Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Sensor and Actuator Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/jsan12040053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sensor and Actuator Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jsan12040053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Sensorless Estimation of Human Joint Torque for Robust Tracking Control of Lower-Limb Exoskeleton Assistive Gait Rehabilitation
Patients suffering from motor disorders or weakness resulting from either serious spinal cord injury or stroke often require rehabilitation therapy to regain their mobility. In the lower limbs, exoskeletons have two motors aligned with the patients’ hip and knee to assist in rehabilitation exercises by supporting the patient’s body structure to increase the torques at the hip and knee joints. Assistive rehabilitation is, however, challenging, as the human torque is unknown and varies from patient to patient. This poses difficulties in determining the level of assistance required for a particular patient. In this paper, therefore, a modified extended state observer (ESO)-based integral sliding mode (ISM) controller (MESOISMC) for lower-limb exoskeleton assistive gait rehabilitation is proposed. The ESO is used to estimate the unknown human torque without application of a torque sensor while the ISMC is used to achieve robust tracking of preset hip and knee joint angles by considering the estimated human torque as a disturbance. The performance of the proposed MESOISMC was assessed using the mean absolute error (MAE). The obtained results show an 85.02% and 87.38% reduction in the MAE for the hip and joint angles, respectively, when the proposed MESOISMC is compared with ISMC with both controllers tuned via LMI optimization. The results also indicate that the proposed MESOISMC method is effective and efficient for user comfort and safety during gait rehabilitation training.
期刊介绍:
Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.