The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint
Abstract
This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.
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Cites
Nonlinear and adaptive control design
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Barrier Lyapunov Functions for the control of output-constrained nonlinear systems
Tee, K. P., Ge, S. S., & Tay, E. H. (2009) · Automatica · doi:10.1016/j.automatica.2008.11.017 · Cited by 2,912
Adaptive Fuzzy Systems and Control: Design and Stability Analysis
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Self-citationWang, D., & Huang, J. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.839354 · Cited by 1,266
Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
Ren, B., Ge, S. S., Tee, K. P., & Lee, T. H. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2047115 · Cited by 1,087
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
Chen, M., Ge, S. S., & Ren, B. (2011) · Automatica · doi:10.1016/j.automatica.2011.01.025 · Cited by 1,056
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
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Wang, T., Gao, H., & Qiu, J. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2411671 · Cited by 636
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Cited by
Li, H., Wu, Y., & Chen, M. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2982168 · Cited by 422
Self-citationNeural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay
Li, D.-P., Liu, Y.-J., Tong, S., Chen, C. L. P., & Li, D.-J. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2799683 · Cited by 382
Li, Y., & Tong, S. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2574913 · Cited by 264
Niu, B., & Li, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2690465 · Cited by 185
Sun, J., Yi, J., & Pu, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3052610 · Cited by 184
Metadata
- Authors
- Zhou, Q., Wang, L., Wu, C., Li, H., & Du, H.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2017-01
- Volume
- 47
- Issue
- 1
- Pages
- 1-12
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2404015798
- Cited by
- 481
- References
- 43
- FWCI
- 60.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhou, Q., Wang, L., Wu, C., Li, H., & Du, H. (2017). Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2016.2557222
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
