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Disturbance observer based iterative learning control for upper limb rehabilitation

Disturbance observer based iterative learning control for upper limb rehabilitation
Disturbance observer based iterative learning control for upper limb rehabilitation
Rehabilitation is essential to recover the motor function of patients after stroke. In clinic cure, voluntary movements are encouraged to accelerate the recovery. However, for the rehabilitation system based on functional electrical stimulation (FES), voluntary movements are unpredictable and act as input disturbance, which would reduce the control precision. In addition, an accurate model of the human musculoskeletal dynamics is usually not available. In this paper, the upper-limb rehabilitation is described first and simplified to a linear nominal model. To deal with the aperiodic voluntary movements and model uncertainty, disturbance observer (DOB) is introduced as the inner-loop of the rehabilitation control system. The suppression of DOB for voluntary movements and model uncertainty is analysed in frequency domain. The stability of DOB is discussed and a criterion is given. To achieve high precision tracking control, iterative learning control (ILC) is employed. Combined with DOB, a variant gain gradient ILC method is designed based on the nominal model, which could enhance the performance and speed up the convergence. To validate the proposed methods, simulations are performed and compared in the end.
2774
IEEE
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Liu, Yanghong
352f8b6c-7e39-4898-a75b-54258a5d1f90
Qin, Yunhui
75bf93ba-824d-48e7-adb0-c1fc75e81152
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Huo, Benyan
21ab083c-b6cb-4b46-bc5e-e73bf8875bce
Liu, Yanghong
352f8b6c-7e39-4898-a75b-54258a5d1f90
Qin, Yunhui
75bf93ba-824d-48e7-adb0-c1fc75e81152
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Huo, Benyan, Liu, Yanghong, Qin, Yunhui, Chu, Bing and Freeman, Christopher (2020) Disturbance observer based iterative learning control for upper limb rehabilitation. In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE. p. 2774 . (doi:10.1109/IECON43393.2020.9254696).

Record type: Conference or Workshop Item (Paper)

Abstract

Rehabilitation is essential to recover the motor function of patients after stroke. In clinic cure, voluntary movements are encouraged to accelerate the recovery. However, for the rehabilitation system based on functional electrical stimulation (FES), voluntary movements are unpredictable and act as input disturbance, which would reduce the control precision. In addition, an accurate model of the human musculoskeletal dynamics is usually not available. In this paper, the upper-limb rehabilitation is described first and simplified to a linear nominal model. To deal with the aperiodic voluntary movements and model uncertainty, disturbance observer (DOB) is introduced as the inner-loop of the rehabilitation control system. The suppression of DOB for voluntary movements and model uncertainty is analysed in frequency domain. The stability of DOB is discussed and a criterion is given. To achieve high precision tracking control, iterative learning control (ILC) is employed. Combined with DOB, a variant gain gradient ILC method is designed based on the nominal model, which could enhance the performance and speed up the convergence. To validate the proposed methods, simulations are performed and compared in the end.

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Published date: 1 July 2020
Venue - Dates: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Online, Singapore, 2020-10-18 - 2020-10-21

Identifiers

Local EPrints ID: 455153
URI: http://eprints.soton.ac.uk/id/eprint/455153
PURE UUID: 3bb5b548-0a1e-4bcc-9507-9d7539e1c9ff
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 10 Mar 2022 20:00
Last modified: 28 Apr 2022 02:06

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Contributors

Author: Benyan Huo
Author: Yanghong Liu
Author: Yunhui Qin
Author: Bing Chu ORCID iD
Author: Christopher Freeman

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