Disturbance observer based iterative learning control for upper limb rehabilitation

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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ORCID for Bing Chu: orcid.org/0000-0002-2711-8717

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