Neural Network Control of a Rehabilitation by State and Output Feedback.pdf (931.32 kB)
Neural network control of a rehabilitation robot by state and output feedback
journal contribution
posted on 2023-06-09, 09:23 authored by Wei He, Shuzhi Sam Ge, Yanan LiYanan Li, Effie Chew, Yee Sien NgIn this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.
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Publication status
- Published
File Version
- Accepted version
Journal
Journal of Intelligent & Robotic SystemsISSN
0921-0296Publisher
Springer VerlagExternal DOI
Issue
1Volume
80Page range
15-31Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-12-15First Open Access (FOA) Date
2017-12-15First Compliant Deposit (FCD) Date
2017-12-14Usage metrics
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