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• W3120903469 abstract "This article deals with the issue of tracking a reference optimal trajectory for an autonomous nonlinear vehicle model by designing both lateral and longitudinal robust feedback control and a suited feedforward control. In previous works, a strategy based on a human-driver field of view was used to plan an optimal trajectory reference. The optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events by using a point mass model. Here, the previously developed GA-PF planification process is integrated in a new complete global planning and tracking method and applied to a validation nonlinear vehicle model. This control tracking method is developed in two strategies: a bicycle model is used as model inversion for feedforward design and a robust control is designed as feedback control in order to take the vehicle (mass and velocity) and road (slope and adherence) parameter variations into account. A lateral nonlinear control and a longitudinal robust control are designed. Realistic autonomous car simulation results are provided on an overtaking scenario and a round-about scenario." @default.
• W3120903469 created "2021-01-18" @default.
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• W3120903469 date "2022-05-01" @default.
• W3120903469 modified "2023-10-04" @default.
• W3120903469 title "Optimal Trajectory Planning and Robust Tracking Using Vehicle Model Inversion" @default.
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• W3120903469 doi "https://doi.org/10.1109/tits.2020.3045917" @default.
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