Multiple Model Adaptive Control of Nonlinear Systems Based on Dynamic Optimization-Reference-Cited by-同舟云学术

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Multiple Model Adaptive Control of Nonlinear Systems Based on Dynamic Optimization

Published:2022-12 Issue:6 Volume:56 Page:509-518
ISSN:0146-4116
Container-title:Automatic Control and Computer Sciences
language:en
Short-container-title:Aut. Control Comp. Sci.

Author:

Weiqiang Tang ,Long Wenkun,Xu Tianpeng,Gao Haiyan

Publisher

Allerton Press

Subject

Signal Processing,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.3103/S0146411622060098.pdf

Reference28 articles.

1. Skach, J., Kiumarsi, B., Lewis, F.L., and Straka, O., Actor-critic off-policy learning for optimal control of multiple-model discrete-time systems, IEEE Trans. Cybern., 2016, vol. 48, no. 1, pp. 29–40. https://doi.org/10.1109/TCYB.2016.2618926

2. Huang, M., Wang, X., Lu, Z., Ma, L.-H., Xu, M., and Su, H.-Ye, Multiple model-based event-triggered adaptive control of a class of discrete-time nonlinear systems, Int. J. Syst. Sci., 2019, vol. 50, no. 7, pp. 1353–1367. https://doi.org/10.1080/00207721.2019.1615569

3. AlandiHallaj, M.A. and Assadian, N., Multiple-horizon multiple-model predictive control of electromagnetic tethered satellite system, Acta Astronaut., 2019, vol. 157, pp. 250–262. https://doi.org/10.1016/j.actaastro.2018.11.003

4. Shi, K., Cheng, D., Yuan, X., Liu, L., and Wu, L., Interacting multiple model-based adaptive control system for stable steering of distributed driver electric vehicle under various road excitations, ISA Trans., 2020, vol. 103, pp. 37–51. https://doi.org/10.1016/j.isatra.2020.03.021

5. Zhang, W., Li, Q., Zhang, Y., Lu, Z., and Nian, Ch., Weighted multiple model adaptive boundary control for a flexible manipulator, Sci. Prog., 2020, vol. 103, no. 1. https://doi.org/10.1177/0036850419886468

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