Risk and Comfort Management for Multi-Vehicle Navigation using a Flexible and Robust Cascade Control Architecture

Abstract : — This paper presents a new cascade control architecture formulation for addressing the problem of autonomous vehicle trajectory tracking under risk and comfort constraints. The integration of these constraints has been split between an inner and an outer loop. The former is made of a robust controller dedicated to stabilizing the car dynamics while the latter uses a nonlinear Model Predictive Control (MPC) scheme to control the car trajectory. The proposed structure aims to take into account several important aspects, such as robustness considerations and disturbance rejection (inner loop) as well as control signal and state constraints, tracking error monitoring and tracking error prediction (outer loop). The proposed design has been validated in simulation while comparing mainly with common kinematic trajectory controllers.
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Communication dans un congrès
IEEE European Conference on Mobile Robots (ECMR), Sep 2017, Paris, France. pp.252 -- 258, 2017, Mobile Robots (ECMR), 2017 European Conference on. 〈10.1109/ECMR.2017.8098703〉
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Soumis le : vendredi 23 février 2018 - 00:31:20
Dernière modification le : lundi 26 février 2018 - 17:06:16

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Charles Philippe, Lounis Adouane, Benoit Thuilot, Antonios Tsourdos, Hyo-Sang Shin. Risk and Comfort Management for Multi-Vehicle Navigation using a Flexible and Robust Cascade Control Architecture. IEEE European Conference on Mobile Robots (ECMR), Sep 2017, Paris, France. pp.252 -- 258, 2017, Mobile Robots (ECMR), 2017 European Conference on. 〈10.1109/ECMR.2017.8098703〉. 〈hal-01658564〉

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