The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Abstract
This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov's stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.
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Citations
Cites
The central nervous system stabilizes unstable dynamics by learning optimal impedance
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001) · Nature · doi:10.1038/35106566 · Cited by 1,097
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Self-citationAdaptive Neural Control of Underactuated Surface Vessels With Prescribed Performance Guarantees
Dai, S.-L., He, S., Wang, M., & Yuan, C. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876685 · Cited by 320
Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation
He, W., Gao, H., Zhou, C., Yang, C., & Li, Z. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.2975232 · Cited by 284
Self-citationAccurate Trajectory Tracking of Disturbed Surface Vehicles: A Finite-Time Control Approach
Wang, N., Karimi, H. R., Li, H., & Su, S.-F. (2019) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2019.2906395 · Cited by 258
Li, D.-P., Li, D.-J., Liu, Y.-J., Tong, S., & Chen, C. L. P. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2707178 · Cited by 148
Metadata
- Authors
- He, W., & Dong, Y.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2018-04
- Volume
- 29
- Issue
- 4
- Pages
- 1174-1186
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2591999327
- Cited by
- 642
- References
- 1
- FWCI
- 58.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- He, W., & Dong, Y. (2018). Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2017.2665581
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