The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation
Abstract
In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown system function. To efficiently tackle the unknown external disturbance, a nonlinear disturbance observer (NDO) is developed. The developed NDO can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set. Using NDO and RBFNN, the DSC scheme is developed for uncertain nonlinear systems based on a backstepping method. Using a DSC technique, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, which is specially important for designs using neural network approximations. Under the proposed DSC scheme, the ultimately bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results are given to show the effectiveness of the proposed DSC design using NDO and RBFNN.
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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 2,445
Disturbance Observer Based Control for Nonlinear Systems
Chen, W.-H. (2004) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2004.839034 · Cited by 1,452
Sliding-Mode Control for Systems With Mismatched Uncertainties via a Disturbance Observer
Yang, J., Li, S., & Yu, X. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2183841 · Cited by 1,332
Wang, D., & Huang, J. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.839354 · Cited by 1,266
Wen, C., Zhou, J., Liu, Z., & Su, H. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2122730 · Cited by 1,199
Stable Adaptive Neural Network Control
Ge, S. S., Hang, C. C., Lee, T. H., & Zhang, T. (2002) · Springer eBooks · doi:10.1007/978-1-4757-6577-9 · Cited by 1,149
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
Chen, M., Ge, S. S., & Ren, B. (2011) · Automatica · doi:10.1016/j.automatica.2011.01.025 · Cited by 1,056
Self-citationChen, M., Ge, S. S., & How, B. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2042611 · Cited by 732
Self-citationLi, H., Yu, J., Hilton, C., & Liu, H. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2202354 · Cited by 726
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Cited by
Sun, J., Yi, J., & Pu, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3052610 · Cited by 184
Adaptive Intelligent Control of Nonaffine Nonlinear Time-Delay Systems With Dynamic Uncertainties
Wang, H., Sun, W., & Liu, P. X. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2627048 · Cited by 151
Bai, L., Zhou, Q., Wang, L., Yu, Z., & Li, H. (2017) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.2780 · Cited by 59
Hua, Z. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-024-10247-0 · Cited by 20
Metadata
- Authors
- Chen, M., Tao, G., & Jiang, B.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2015-09
- Volume
- 26
- Issue
- 9
- Pages
- 2086-2097
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2041069066
- Cited by
- 508
- References
- 26
- FWCI
- 30.15
- Type
- journal article
- Language
- en
- Open access
- closed
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