The DOI matches its Crossref record; 71 of the 72 references it cites were found in a registry or an index.
Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints
Abstract
This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.
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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 [27]
Citations
Cites
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 2,920
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
He, W., Chen, Y., & Yin, Z. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2411285 · Cited by 1,279
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 1,088
Control of nonlinear systems with time-varying output constraints
Tee, K. P., Ren, B., & Ge, S. S. (2011) · Automatica · doi:10.1016/j.automatica.2011.08.044 · Cited by 1,065
Adaptive Neural Control of Uncertain MIMO Nonlinear Systems
Ge, S. S., & Wang, C. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.826130 · Cited by 971
Liu, Y., & Tong, S. (2015) · Automatica · doi:10.1016/j.automatica.2015.10.034 · Cited by 885
Self-citationLiu, Y., & Tong, S. (2016) · Automatica · doi:10.1016/j.automatica.2016.10.011 · Cited by 831
Self-citationChen, M., Ge, S. S., & How, B. V. E. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2042611 · Cited by 733
Adaptive Neural Control of Nonlinear Time-Delay Systems With Unknown Virtual Control Coefficients
Ge, S. S., Hong, F., & Lee, T. H. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2003.817055 · Cited by 717
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
He, W., & Dong, Y. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2665581 · Cited by 643
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Liu, L., Liu, Y., & Tong, S. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2828308 · Cited by 481
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
Self-citationAdaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States
Liu, Y., Zhao, W., Liu, L., Li, D., Tong, S., & Chen, C. L. P. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3107600 · Cited by 334
Self-citationTime-varying IBLFs-based adaptive control of uncertain nonlinear systems with full state constraints
Liu, L., Gao, T., Liu, Y., Tong, S., Chen, C. L. P., & Ma, L. (2021) · Automatica · doi:10.1016/j.automatica.2021.109595 · Cited by 321
Self-citationLiu, Y., Zeng, Q. H., Tong, S., Chen, C. L. P., & Liu, L. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2893847 · Cited by 291
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Metadata
- Authors
- Li, D.-P., Li, D.-J., Liu, Y.-J., Tong, S., & Chen, C. L. P.
- Affiliations
- Liaoning University of Technology (CN)
- University of Macau (MO)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2017-10
- Volume
- 47
- Issue
- 10
- Pages
- 3100-3109
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2625591652
- Cited by
- 148
- References
- 72
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 25 · 2020: 21 · 2019: 26 · 2018: 26 · 2017: 6
- FWCI
- 13.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61603164, 61473139, 61622303)
- Project for Distinguished Professor of Liaoning Province
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Neural Networks Stability and Synchronization
- Keywords
- tracking control, MIMO nonlinear systems, unknown time-varying delays, full-state constraints, adaptive neural network control, Lyapunov-Krasovskii functional, separation processes, barrier Lyapunov function, signal function, closed-loop boundedness
- MeSH terms
- Computer Simulation, Signal Processing, Computer-Assisted, Time Factors, Neural Networks, Computer, Nonlinear Dynamics
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