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
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A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
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Items 101–130 of 130
Zhang, W., Xia, Q., & Wei, L. (2020) · The Aeronautical Journal · doi:10.1017/aer.2020.28 · Cited by 7
“If λmin (Q) ‖η‖(p−2)/(p−1) − ḋmax (x) ‖B‖ > 0, Equation (26) can be written as V̇1 ≤ − γ V 1/2 1 √ λmax (P) , · · · (27) where γ = λmin (Q) ‖η‖(p−2)/(p−1) − ḋmax (x) ‖B‖ > 0, In the light of Lemma 1, the area ‖η‖ ≤ ( ḋmax (x) ‖B‖ λmin (Q) )(p−1)/(p−2) , · · · (28)”
MethodController Design for MIMO System with Time Delay Using Closed-loop Gain Shaping Algorithm
Zhang, Z., & Zhang, X. (2019) · International Journal of Control Automation and Systems · doi:10.1007/s12555-018-0574-z · Cited by 7
Adaptive Linearizing Fuzzy Control of Delayed MIMO Nonstrict-Feedback Systems
Huang, J., & Law, Y.-C. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2945233 · Cited by 6
“With a slight modification of [35], the system nonlinearities are given by f with τ 1 , 1 = 2 + 0 .”
BackgroundMethodInfluentialA Constraint-Programmed Planner for Deep Space Exploration Problems With Table Constraints
Jiang, X. W., & Xu, R. (2017) · IEEE Access · doi:10.1109/access.2017.2744262 · Cited by 6
“, space-based observation systems (the Hubble Space Telescope [4]), and the ESA’s projects for on-board autonomy (the Proba series of satellites [5]–[8]).”
BackgroundOn p-th Moment Exponential Stability for Stochastic Cellular Neural Networks with Distributed Delays
Xu, C., Chen, L., & Li, P. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-017-0570-8 · Cited by 6
Kharrat, M., & Alhazmi, H. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11749-7 · Cited by 5
Adaptive Neural Tracking Control of Nonlinear Nonstrict-Feedback Systems With Unmodeled Dynamics
Zhao, Y., Niu, B., Wang, H., & Yang, D. (2019) · IEEE Access · doi:10.1109/access.2019.2926558 · Cited by 5
“Such as [24], an adaptive tracking controller was designed for a class of nonstrictfeedback nonlinear time-delay systems.”
MethodYang, Y., Fan, X., Sun, B., Xu, C., Zuo, S., & Yue, D. (2021) · International Journal of Systems Science · doi:10.1080/00207721.2021.1947410 · Cited by 5
“X. Zhao et al. (2016) proposed an adaptive neural tracking control strategy based on NNs technology for a multiple-input-multiple-output (MIMO) system.”
MethodCui, E., Jing, Y., & Gao, X. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/7368092 · Cited by 5
Villegas, F. J., Hecker, R. L., & Peña, M. (2018) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1177/0959651818771481 · Cited by 4
Huang, J. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.10.036 · Cited by 4
Zhang, J., Cong, M., Liu, D., Du, Y., & Ma, H. (2021) · Assembly Automation · doi:10.1108/aa-08-2020-0109 · Cited by 4
A Semi-Supervision Fault Diagnosis Method Based on Attitude Information for a Satellite
Zhang, Z., She, Z., & Zhang, A. (2017) · IEEE Access · doi:10.1109/access.2017.2758377 · Cited by 4
“Many technologies have been employed in this field, such as adaptive control [10], [11] and slide mode control [12], [13].”
MethodWang, X., Wu, Q., Wang, H., & Yin, X. (2019) · IET Control Theory and Applications · doi:10.1049/iet-cta.2018.5353 · Cited by 4
Hu, L., Wang, X., & Li, H. (2017) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331217707368 · Cited by 3
“When completing the complex mission (Zhao et al., 2016a, 2015b), cooperative control among systems has a better effect compared to the control of each system.”
BackgroundMethodZhao, Y., Liu, C., Liu, X., Zhou, S., Zheng, H., Wang, H., & Zhou, Y. (2023) · IET Control Theory and Applications · doi:10.1049/cth2.12475 · Cited by 3
Xu, Y., Wang, R., Lu, H., Song, X., Deng, Y., & Zhou, W. (2021) · Complexity · doi:10.1155/2021/9957624 · Cited by 3
Adaptive fuzzy control design for a class of uncertain switched lower triangular systems
Xu, N., & Wang, X. (2017) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331217713838 · Cited by 2
“It has been widely used to solve some complex problems and applied in the fields of industry and engineering (Peng et al., 2015; Xu and Zhang, 2015; Yu et al., 2015a; Zhao et al., 2016b).”
BackgroundMethodProviding Definitive Learning Direction for Relation Classification System
Qin, P., Xu, W., & Guo, J. H. (2017) · Journal of Control Science and Engineering · doi:10.1155/2017/3924641 · Cited by 1
“In order to cope with the variety of natural language, many complicated statistical and analysis methods should be considered [8, 9].”
BackgroundRobust fuzzy stabilization of hybrid discrete delay T–S systems
Mahmoud, M. S. (2017) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2017.09.025 · Cited by 1
Transformer Fault Diagnosis Model Based on Discrete Hopfield Neural Network
Ding, S., Cheng, Z., Wu, Q., Zhang, F., & Yang, Y. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-98776-7_152 · Cited by 1
Pole Path Assignment of Constrained SISO Affine Nonlinear Systems
Kaheni, M., Zarif, M. H., Kalat, A. A., & Fadali, M. S. (2019) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-019-00203-9 · Cited by 1
Adaptive backstepping neural control for nonlinear systems with quantized input signals
Liu, W. (2017) · Chinese Automation Congress · doi:10.1109/cac.2017.8242917 · Cited by 1
“Compared with the existing results [7], [17], in which the systems are without quantized input signals, the new adaptive control scheme designed in this work can deal with the control problem for nonlinear systems subject to input quantization.”
MethodResultYue, H., Zhang, W., & Chen, Q. (2022) · International Journal of Fuzzy Systems · doi:10.1007/s40815-022-01405-w · Cited by 1
“In the few years, for the systems with input delay, some results have been received [32–39].”
ResultLi, Z., Ren, X., Dong, B., Chen, H., & Zhang, W. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10056105 · Cited by 1
Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs
Wang, J., Zhu, L., & Xiao-ping, S. (2017) · Complexity · doi:10.1155/2017/5628932
Robust backstepping stabilization of nonlinear systems with time-varying parameters
Kabziński, J., & Mosiołek, P. (2017) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2017.8046928
“The first approach was to approximate plant nonlinearities by neural networks [2][3], fuzzy systems [4], or any other type of approximation, in order to deal with the approximation parameters (weights) rather than with the original parameters.”
BackgroundRobust control for a class of uncertain nonlinear systems using adaptive filters
Xiao, D., & Liu, Y. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8407505
“Particularly, as an efficient design methodology, adaptive backstepping approach has been studied extensively for large classes of uncertain low triangular systems [6–13].”
MethodGuan, C., Sun, D. B., Fei, Z., & Ahn, C. K. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483416
“INTRODUCTION Over the past decade, an increasing interest has been paid to the research of neural networks since their powerful ability in describing the complex nonlinear dynamics of systems and extensive applications to engineering practice, such as combinational optimization, static image processing, associate memo…”
BackgroundFunnel tracking control for nonlinear servo drive systems with unknown disturbances
Wang, S., Na, J., & Ren, X. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-315574-1.00024-5
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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