The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Adaptive neural control for non-strict-feedback nonlinear systems with input delay
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In the citing paper
“The observation that the design of nonlinear systems in nonstrict-feedback form that incorporates input delay and hysteresis similar to backlash is still an unresolved difficulty in the previously discussed outcomes is what inspired this research.”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [28]
In the citing paper
“The presence of input delays can potentially degrade system performance or lead to instability [28].”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [28]
In the citing paper
“To address this challenge, radial basis function neural networks with Gaussian function as an activation functions satisfying 0 < P T i (•)P i (•) ≤ N have been implemented to manage such complexities [28,47,49].”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [28]
Citations
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Items 101–152 of 152
Zhang, X., Tan, J., & Wu, J. M. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-09544-5 · Cited by 4
Wang, J., & Wang, Y. (2022) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3521 · Cited by 4
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Wang, J., Peng, Y., & Dou, L. (2021) · International Journal of Control Automation and Systems · doi:10.1007/s12555-020-0768-z · Cited by 4
“On the other hand, communication time-delays are also common in the real system [20–23].”
BackgroundZheng, X. (2023) · Asian Journal of Control · doi:10.1002/asjc.3163 · Cited by 4
“In [35, 36], an adaptive neural network (NN) control method was proposed for nonlinear systems with input delay by introducing a suitable auxiliary system to compensate for the input delay.”
BackgroundMethodMuritala, I. O., Bello-Salau, H., Salawudeen, A. T., & Mohammed, S. (2023) · Franklin Open · doi:10.1016/j.fraope.2023.100035 · Cited by 4
Zhang, X., Tan, J., Yao, Y., & Wu, J. M. (2021) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3364 · Cited by 4
“More recently, in Reference 29, the problem of adaptive neural control for non-strict-feedback nonlinear systems with input delay has been studied.”
BackgroundWang, S., & Wu, L. (2025) · International Journal of Systems Science · doi:10.1080/00207721.2025.2506009 · Cited by 3
“Specifically, the negative effects of input delays were eliminated by introducing an additional system and using the Lyapunov-Krasovskii function in H. Q. Wang et al. (2020).”
Kharrat, M., & Alhazmi, H. (2025) · Circuits Systems and Signal Processing · doi:10.1007/s00034-025-03153-2 · Cited by 3
“Otherresearchhasaddressedadaptivecontrol fornonstrict-feedbacknonlinearsystemswithinputdelay,employingneuralnetwork approximationtechnique[30],aswellasadaptiveneuralnetworkcontroldesignfor nonlinearsystemswithstateconstraints[14].”
Zeng, Q. H., Shi, Q., Liu, L., & Liu, Y. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113146 · Cited by 3
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Kharrat, M., & Mercorelli, P. (2025) · Mathematics · doi:10.3390/math14010014 · Cited by 3
Benslimane, H. (2023) · Journal of Control Automation and Electrical Systems · doi:10.1007/s40313-023-01060-y · Cited by 3
“At present, the adaptive backstepping technique is widely used as a design control method for non-strict feedback uncertain nonlinear systems (Li et al., 2017; Wang et al., 2020; Yudi Wang et al., 2022; Wu et al., 2022; Yang et al., 2022; Zhang et al., 2020).”
InfluentialSpatial-temporal response of the regional ecological quality to urban settlement development
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“The evolution of the urban regional landscape can be verified from the studies on land use and construction land of the Urban Cluster in Mid-inner Zhejiang (Feng et al., 2021; J. F. Wang et al., 2020; J. M. Wang et al., 2018).”
Yue, H., Zhang, W., & Chen, Q. (2022) · Journal of low frequency noise, vibration and active control · doi:10.1177/14613484221109132 · Cited by 3
“In, 18 Wang et al. deployed neural networks to handle non-strict functions based on their strong approximation capacity, which simpli fi es processing for non-strict feedback systems.”
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Han, Z., Sun, Z., Li, J., Chen, C., & Meng, Q. (2025) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.4011 · Cited by 2
“Because NNs and FLSs have very strong approximation ability, some scholars have proposed the neural networks [14–16] and fuzzy logic systems [17–19].”
Kharrat, M. (2025) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.70235 · Cited by 2
“One of the most important techniques in non-linear control among them is adaptive backstepping [9, 10].”
Adaptive Fuzzy Fixed‐Time Attitude Tracking Control for Rigid Spacecraft With Input Delay
Wang, H., & Feng, Y. (2025) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.70012 · Cited by 2
Self-citationKharrat, M., & Mercorelli, P. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07085-5 · Cited by 2
“For any ϵ > 0 , the following equation holds [ 14] ˆ f where ∈ with N > 1 denotes the weight vector, and W ∗ is defined by where S ( Z ) = [ s 1 ( Z ) , s 2 ( Z ) , . . . , s N ( Z )] ⊤ is the basis function vector with s i ( Z ) given by the Gaussian function: where µ i = [ µ i 1 , µ i 2 , . . . ,…”
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Adaptive nonlinear robust lead compensator design for a class of uncertain nonlinear systems
Mhmood, A. H., & Rakan, A. B. (2024) · International Journal of Dynamics and Control · doi:10.1007/s40435-024-01456-x · Cited by 2
“Furthermore, a state feedback stabilizing controller that combines backstepping and adaptive neural control was developed to guarantee that all variables in the closed-loop system remain semiglob-ally even and ultimately limited inside a narrow region near the point of origin [10].”
Kharrat, M., & Alhazmi, H. (2025) · International Journal of General Systems · doi:10.1080/03081079.2025.2577741 · Cited by 2
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Adaptive State-Quantized Control of Uncertain Lower-Triangular Nonlinear Systems with Input Delay
Yoo, S. J. (2021) · Mathematics · doi:10.3390/math9070763 · Cited by 2
Si, W., Dong, X., & Dong, Y. (2022) · International Journal of Systems Science · doi:10.1080/00207721.2022.2025501 · Cited by 2
“For this paper, the main features are described as follows: (1) The novel C Wang et al. (2020) did not consider stochastic disturbances, nor did they implement feasibility removal for constraint problems.”
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Zheng, X., & Yan, H. (2025) · Circuits Systems and Signal Processing · doi:10.1007/s00034-025-03084-y · Cited by 1
“Combining (1), (4) and (6), we obtain d e = ( A 0 e + (cid:4) F ) d t + H ( y ) d ω, Then, an auxiliary system [50] with the same order as that of the considered system can be constructed to handle the influence of the input delay as where , and the initial condition of this system is”
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Huang, C., Zhou, K., Zhou, K., & Su, Z. (2025) · International Journal of Naval Architecture and Ocean Engineering · doi:10.1016/j.ijnaoe.2025.100691 · Cited by 1
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“To this end, recent studies have developed adaptive control strategies for nonlinear systems with unknown and time-varying input delays, incorporating methods such as backstepping design, function approximation, and delay estimation [40, 41].”
Wang, Y., Chen, J., & Ma, W. (2026) · Scientific Reports · doi:10.1038/s41598-026-35313-y · Cited by 1
“To address the design challenges posed by the non-strict feedback structure, methods such as variable separation 6–8 , function transformation 9,10 , finite-time 11–17 , and fixed-time 18–22 have been proposed successively.”
Lin, C. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191712 · Cited by 1
RetractedZhou, Y., & Sun, H. (2020) · International Journal of Control Automation and Systems · doi:10.1007/s12555-019-0969-5 · Cited by 1
Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation
Jiang, X., Liu, M., Liu, S., Xu, J., & Liu, L. (2020) · Discrete Dynamics in Nature and Society · doi:10.1155/2020/7259613 · Cited by 1
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Funnel Control of Uncertain Flexible Air-breathing Hypersonic Vehicles
Qi, Q., & Bu, X. (2020) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus50048.2020.9274977
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Self-citationEvent-triggered fixed-time adaptive neural control for time-delay nonlinear systems
Wu, P., & Liu, W. (2023) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isas59543.2023.10164518
Adaptive control of strict-feedback nonlinear systems with output constraints and input delays
Wang, P., Wu, L., Wang, S., Yu, Q.-K., Zhang, L., & Li, M. S. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10588180
Han, X., & Feng, M. (2025) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20240423001488
“At the same time, different output feedback controllers have been designed for nonlinear systems with unmeasurable states by using the state observer [11, 12].”
Guo, Y., Zheng, Z., Wang, Y., Zhu, G., Li, C., & Wang, X. (2026) · Space Science & Technology · doi:10.34133/space.0367
Exponential Stability of Nonlinear Impulsive Time-Delay Systems with Sparse Impulsive Instants
Chen, Y., Wei, G., & Liang, J. (2026) · Circuits Systems and Signal Processing · doi:10.1007/s00034-026-03639-7
Parameter Tuning of a Direct Neuro-Self Tuning Controller for Nonlinear System
Cho, W. C., Kim, B., & Li, H. Y. (2026) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2026.24.5.103
Zong, Z.-Y., Guan, F., Zhu, S.-L., Li, C., Sun, Q., Qing-hua, Z., & Han, Y. (2026) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2026.110528
Metadata
- Authors
- Wang, H., Liu, S., & Yang, X.
- Affiliations
- Bohai University (CN)
- Harbin Institute of Technology (CN)
- Corresponding author
- Wang, Huanqing
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 514
- Pages
- 605-616
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2988820939
- Cited by
- 152
- References
- 47
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 24 · 2023: 19 · 2022: 15 · 2021: 23 · 2020: 39
- FWCI
- 10.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Sciences (publishedVersion)
- Funders
- Program for Liaoning Innovative Talents in University (LR2016040)
- Taishan Scholar Project of Shandong Province (2015162, tsqn201812093)
- Natural Science Foundation of Liaoning Province (20180550590, 20180550691)
- National Natural Science Foundation of China (61773072, 61773073)
- 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, input delay, auxiliary systems, backstepping control, state feedback stabilization, semi-global uniform ultimate boundedness, intelligent control
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