Abstract
This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the existing literature on CACC, a data-driven optimal control policy is developed by global adaptive dynamic programming (GADP). Interestingly, the developed control policy achieves global stabilization of the nonlinear vehicular platoon system in the absence of the a priori knowledge of system dynamics. Numerical simulation results are presented to validate the effectiveness of the developed approach.
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This work has been supported in part by the U.S. National Science Foundation grant ECCS-1501044.
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Gao, W., Jiang, ZP. (2017). Data-Driven Nonlinear Adaptive Optimal Control of Connected Vehicles. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_13
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DOI: https://doi.org/10.1007/978-3-319-70136-3_13
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