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Adaptive Intelligent Control of Nonaffine Nonlinear Time-Delay Systems With Dynamic Uncertainties
Abstract
Adaptive neural intelligent control is investigated for a class of pure-feedback nonlinear time-delay systems with unmodeled dynamics in nonlower-triangular form, which views the lower-triangular structure as a special structure. A variable partition technique is applied to surmount the difficulty in the nonlinear functions of whole state variables. By utilizing the backstepping recursive design approach and the universal approximation capability of neural networks, an adaptive neural controller is systemically designed. Then, based on the utilization of Lyapunov-Krasovskii functionals, the semiglobally uniform boundedness of all closed-loop signals is guaranteed. Finally, the suggested control method is verified through a numerical example. The main advantage of this paper is that an intelligent control method is developed for pure-feedback nonlinear systems with state time delay, unmodeled dynamics and nonlower triangular form. Further developments will focus on how to deal with the problem of output feedback control of pure-feedback nonlinear time-delay systems with unmodeled dynamics and nonlower-triangular structure.
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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 [26]
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“where the equivalent control signal and FCMAC estimator are the same as that in equations (35) and (31), respectively.”
BackgroundResultStatic Output Feedback Stabilization of a Class of Switched Linear Systems with State Constraints
Su, Q., Zhu, H., & Li, J. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-016-0800-5 · Cited by 11
Li, M., Li, S., Zhang, J., Wu, F., & Zhang, T. (2022) · Entropy · doi:10.3390/e24081028 · Cited by 11
Yu, J., Sun, Y., & Li, Z. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.01.023 · Cited by 11
Lee, S.-D., & Jung, S. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-017-0187-y · Cited by 11
Mean First Passage Time of Preferential Random Walks on Complex Networks with Applications
Zheng, Z., Xiao, G., Wang, G., Zhang, G., & Jiang, K. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/8217361 · Cited by 10
“Such processes have potential applications in the control of stochastic systems [7–10].”
BackgroundJiang, K., Niu, B., Li, J., Duan, P., Wang, J., & Yang, D. (2018) · IEEE Access · doi:10.1109/access.2018.2877798 · Cited by 10
“For example, the paper [55] applied the variable separation technology to overcome the dsign difficulties of non-strict-feedback nonlinear functions and successfully constructed an adaptive intelligent controller for a class of nonaffine nonlinear time-delay systems.”
MethodLiu, J., Zhao, M., Li, G., & Chen, J. (2020) · IEEE Access · doi:10.1109/access.2020.3020980 · Cited by 10
“Adaptive control method is widely applied to uncertain systems, especially for controlling strict-feedback nonlinear systems [12].”
MethodLi, Z., Cao, L., Pan, Y., & Zhang, P. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.09.033 · Cited by 10
Random Search Enhancement of Incremental Regularized Multiple Hidden Layers ELM
Liu, J., Liu, X., Liu, C., Le, B. T., & Xiao, D. (2019) · IEEE Access · doi:10.1109/access.2019.2905077 · Cited by 10
Optimal Control Method for Robot-Tracking Based on Control-Lyapunov-Function
Wu, Y., Liu, L., Yang, Y., & Dai, S. (2019) · IEEE Access · doi:10.1109/access.2019.2923658 · Cited by 10
“Want H et al employ Adaptive Neural Output-Feedback Control to deal these problems with unknown external disturbance [8].”
MethodReachable set estimation for neutral Markovian jump systems with mode‐dependent time‐varying delays
Jiang, X., Xia, G., Feng, Z., & Jiang, Z. (2020) · Optimal Control Applications and Methods · doi:10.1002/oca.2670 · Cited by 9
“P{vt+Δt = n|vt = s} = {σsnΔt + o(Δt), s ≠ n, 1 + σssΔt + o(Δt), s = n, (2)”
Metadata
- Authors
- Wang, H., Sun, W., & Liu, P. X.
- Affiliations
- Carleton University (CA)
- Bohai University (CN)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2017-07
- Volume
- 47
- Issue
- 7
- Pages
- 1474-1485
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2558651951
- Cited by
- 151
- References
- 63
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 4 · 2023: 7 · 2022: 14 · 2021: 7 · 2020: 13 · 2019: 20 · 2018: 33 · 2017: 38
- FWCI
- 25.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (61304002, 61175072, 61473160)
- Natural Science Foundation of Liaoning Province of China (2015020058)
- 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, pure-feedback nonlinear systems, time-delay systems, unmodeled dynamics, backstepping control, Lyapunov-Krasovskii functional, state delay
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