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Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free

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Abstract

This paper focuses on a distributed adaptive singularity-free fixed-time neural network tracking control problem for vehicular platoon with model uncertainties. The reference trajectory of platoon is modeled based on the actual driving conditions including four stages. Moreover, the adaptive neural network and \(H_\infty \) control theory are adopted to tackle unknown nonlinearities and mismatched complete disturbances of third-order vehicle dynamics. Bying integrating fixed-time control with backstepping technology, a distributed adaptive singularity-free fixed-time control protocol is constructed. Meanwhile, a smooth switching function is designed to effectively deal with the conventional fixed-time singularity problem caused by differentiation of a virtual control law. Compared with the existing results, both cases of the designed switching function are practically fixed-time stable. Finally, the effectiveness of the presented control strategy is further attested by simulation experiments of four different scenarios that may take place in actual traffic, including simulation comparisons and one noise analysis.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant 62003097, 62103214. In part by Talent Introduction and Cultivation Plan for Youth Innovation of Universities in Shandong Province.

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Correspondence to Yang Liu.

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An, J., Liu, Y., Sun, J. et al. Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free. Neural Comput & Applic 35, 2527–2540 (2023). https://doi.org/10.1007/s00521-022-07725-0

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