The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay
Abstract
In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.
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“Traditional methods based on Lyapunov-Krasovskii functionals are generally less effective in managing input delays compared to state delays [25][26][27].”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [25]
Citations
Cites
Dynamic surface control for a class of nonlinear systems
Swaroop, D., Hedrick, J. K., Yip, P. P., & Gerdes, J. C. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2000.880994 · Cited by 2,454
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2301773 · Cited by 1,724
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Chen, M., Ge, S. S., & How, B. V. E. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2042611 · Cited by 733
Wang, T., Gao, H., & Qiu, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2411671 · Cited by 636
Tong, S., Sui, S., & Li, Y. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2014.2327987 · Cited by 554
Tong, S., Li, Y., Feng, G., & Li, T. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2108283 · Cited by 498
Robust and Adaptive Backstepping Control for Nonlinear Systems Using RBF Neural Networks
Li, Y., Qiang, S., Zhuang, X., & Kaynak, O. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.826215 · Cited by 456
Wang, H., Chen, B., Liu, X., Liu, K., & Lin, C. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2240296 · Cited by 445
Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach
Chen, B., Liu, X. P., Ge, S. S., & Lin, C. (2012) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2012.2190048 · Cited by 428
Cited by
Sorted by citations
Yu, J., Zhao, L. H., Yu, H., & Lin, C. (2019) · Automatica · doi:10.1016/j.automatica.2019.03.022 · Cited by 285
Yu, J., Shi, P. J., Lin, C., & Yu, H. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2901250 · Cited by 228
Adaptive Fuzzy Prescribed Performance Control of Nontriangular Structure Nonlinear Systems
Li, Y., Shao, X., & Tong, S. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2937046 · Cited by 211
“In addition, there exists another strict limit in the nontriangular structure nonlinear systems [21]–[28], i.e., in the last equation of the controlled systems, the control input u appears as an affine variable ( ˙ x n = f n (¯ x n ) + g n (¯ x n ) u ).”
BackgroundInfluentialWu, J., Chen, X., Zhao, Q., Li, J., & Wu, Z. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3012607 · Cited by 209
Zhao, X., Yang, H., Xia, W., & Wang, X. (2016) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2594273 · Cited by 203
“Then, in [9], the research is extended to multiinput multioutput (MIMO) nonstrict-feedback nonlinear systems.”
MethodSelf-citation
Metadata
- Authors
- Zhao, X., Yang, H., Karimi, H. R., & Zhu, Y.
- Affiliations
- Bohai University (CN)
- University of Agder (NO)
- Harbin Institute of Technology (CN)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2016-06
- Volume
- 46
- Issue
- 6
- Pages
- 1337-1349
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2353924536
- Cited by
- 130
- References
- 44
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 2 · 2022: 8 · 2021: 15 · 2020: 11 · 2019: 23 · 2018: 30 · 2017: 30 · 2016: 7
- FWCI
- 20.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61203123, 61333012)
- Liaoning Excellent Talents in University (LR2014035)
- Shandong Provincial Natural Science Foundation, China (ZR2012FQ019)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Neural Networks Stability and Synchronization
- Keywords
- adaptive neural network control, MIMO nonlinear systems, nonstrict-feedback systems, time delay, output feedback tracking control, backstepping design, neural network approximation, ultimate boundedness
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