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• W2972649170 abstract "In this paper, we utilize a direct adaptive fuzzy control methodology for the skeleton’s wrist joint, to overcome the deficiencies brought by varied inconstant unmodeled dynamics (such as input saturation, gravity, viscous friction, fuzzy approximation errors, payloads, the high-frequency disturbances). Unlike the classic PID control law, we use the fuzzy law to approach the control information itself. There is no need to calibrate the robot’s precise model information as a prior. Extensive simulation results demonstrate that performance of the platform is improved significantly compared to the traditional control. It turns out that this mechanism also has a very strong robustness against nonlinearities such as the input saturation and unknown nonlinearities. This technique, together with the traditional therapy, might provide a new avenue in terms of improving the robot’s control precision, carrying out intentional and predefined active and passive movements of the contralateral, promoting reconstruction of brain function, shortening the rehabilitation time, and producing objective data for further recovery evaluation." @default.
• W2972649170 created "2019-09-19" @default.
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• W2972649170 date "2019-06-01" @default.
• W2972649170 modified "2023-09-27" @default.
• W2972649170 title "Direct Adaptive Fuzzy Compensation Control of a Skeleton Post-Stroke Rehabilitation Robot’s Wrist Joint With Unmodeled Dynamics" @default.
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• W2972649170 doi "https://doi.org/10.1109/ccdc.2019.8832370" @default.
• W2972649170 hasPublicationYear "2019" @default.
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