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• W4285232622 startingPage "20798" @default.
• W4285232622 abstract "This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$lambda $ </tex-math></inline-formula> -norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods." @default.
• W4285232622 created "2022-07-14" @default.
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• W4285232622 date "2022-11-01" @default.
• W4285232622 modified "2023-09-26" @default.
• W4285232622 title "Robust Adaptive Learning-Based Path Tracking Control of Autonomous Vehicles Under Uncertain Driving Environments" @default.
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• W4285232622 doi "https://doi.org/10.1109/tits.2022.3176970" @default.
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