Tuning MPC Path-following Controllers Using Multi-objective Optimization
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This paper presents a design approach for tuning autonomous driving controllers of road vehicles. In this regard, a bicycle vehicle model and a model predictive control (MPC) algorithm have been applied to the tracking-control lateral of autonomous vehicles. Additionally, in order to maximize ride comfort and minimize path-following error, a multi-objective optimization problem has been formulated. In the multi- objective optimization problem, a meta-heuristic search algorithm, i.e., non-dominated sorting differential evolution (NSDE), is applied, the design objective is to improve ride quality and reduce tracking-control errors, and the design variables are weighting factors of the MPC controller. Numerical simulation is performed to demonstrate the effectiveness of the proposed design approach.
Part of the Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022.
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85
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en