| Citation: | SU Zikang, LI Chuntao, YU Yue, et al. High anti-disturbance trajectory tracking control for cable towed vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2234-2248. doi: 10.13700/j.bh.1001-5965.2020.0379(in Chinese)
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To handle the precise trajectory control problem of the cable towed vehicle under unknown airflow disturbances, the minimal learning parameter neural network estimator based dynamic surface trajectory control method is proposed for the towed vehicle. Firstly, combined with the multi-body dynamic model of the cable towed system, the towed vehicle's six-degree-of-freedom nonlinear model is established and then formulated in the affine nonlinear form. Secondly, considering the comprehensive influence on the towed vehicle by the unknown airflow disturbances (such as the trailing vortex, atmospheric turbulence, gust, etc.) and the variably unmeasurable cable tensions, the minimal learning parameter neural network based state/disturbance online estimators are established to accurately reconstitute the unmeasurable lumped disturbance of system. Thirdly, on the basis of the above state/disturbance online estimators, the minimal learning parameter neural network state/disturbance estimator based dynamic surface trajectory control method is proposed. Finally, the simulation results show that the proposed method can achieve the towed vehicle's trajectory stabilization and maneuvering trajectory tracking control.
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