Open AccessGain‐scheduled steering control for autonomous vehicles
Author(s) -
Kapsalis Dimitrios,
Sename Olivier,
Milanés Vicente,
Martinez John J.
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2020.0698
Subject(s) - control theory (sociology) , weighting , controller (irrigation) , actuator , track (disk drive) , computer science , function (biology) , control (management) , control engineering , engineering , artificial intelligence , evolutionary biology , biology , agronomy , radiology , operating system , medicine
This study presents a linear parameter varying (LPV) approach for the lateral control of autonomous vehicles, in order to take into account the whole operating domain of longitudinal speeds, as well as the variation of the look‐ahead distance. Combining a dynamical vehicle model with look‐ahead dynamics, together with an identified actuator model including an input delay, the closed‐loop performances can be achieved and the tracking capabilities can be improved for every speed. This is obtained in particular through ad hoc representation of the look‐ahead time as a parameter‐dependent function. An H ∞ LPV control problem is formulated considering parameter‐dependent weighting functions, allowing the control adaptation for all speeds. The synthesis is performed using the gridding approach, in order to account for varying parameter rate. The proposed steering control system has been implemented on a real electric Renault Zoe car. The performances are therefore assessed experimentally on a real test track with a varying longitudinal speed profile, and compared with a classical LPV polytopic controller, which proves the advanced lane‐tracking capabilities of the proposed methodology.