Sub-optimally solving actuator redundancy in a hybrid neuroprosthetic system with a multi-layer neural network structure

Author:

Bao Xuefeng,Mao Zhi-Hong,Munro Paul,Sun Ziyue,Sharma NitinORCID

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Science Applications

Reference51 articles.

1. Akpan, V., Hassapis, G.: Nonlinear model identification and adaptive model predictive control using neural networks. ISA Trans. 50(2), 177–194 (2011)

2. Alibeji, N.A., Molazadeh, V., Dicianno, B.E., Sharma, N.: A control scheme that uses dynamic postural synergies to coordinate a hybrid walking neuroprosthesis: theory and experiments. Front. Neurosci. 12, 159 (2018). https://www.frontiersin.org/article/10.3389/fnins.2018.00159 (Online)

3. Alibeji, N.A., Molazadeh, V., Moore-Cligenpeel, F., Sharma, N.: A muscle synergy inspired control design to coordinate functional electrical stimulation and a powered exoskeleton. IEEE Control Syst. Mag. 38, 35–60 (2018) (conditionally accepted)

4. Alibeji, N., Kirsch, N., Sharma, N.: A muscle synergy-inspired adaptive control scheme for a hybrid walking neuroprosthesis. Front. Bioeng. Biotechnol. 3, 203 (2015)

5. Alibeji, N., Kirsch, N., Sharma, N.: An adaptive low-dimensional control to compensate for actuator redundancy and fes-induced muscle fatigue in a hybrid neuroprosthesis. Control Eng. Pract. 59, 204–219 (2017)

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