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Analysis of a Cascaded MPC Structure for Vehicle Motion Control [in press]

Schwartz, Manuel; Ludmann, Laurin; Hohmann, Sören

Abstract (englisch):
Intelligent motion planning and control of automated vehicles and transportation systems have great advantages for increasing safety, energy consumption as well as driving comfort. Hereby, advanced control techniques and algorithms have to be developed meeting the challenging demands for passenger transportation. Therefore, this paper presents a cascaded (nonlinear) Model Predictive Control (MPC) structure for highly automated vehicles for position and velocity reference tracking. One focused topic is the implementation of an algorithm passing the reference trajectories information by the motion planner and the predicted optimal control signals given by the MPCs through the whole vehicle control to maintain the desired vehicle behavior down to the low-level controls. The aim of the paper is to examine the structure, tuning possibilities as well as algorithms for the connection of several MPCs for motion control. Whereby, the interface with input trajectory adaption, interpolation due to sampling time differences and reference correction is examined specifically. Presented controls and algorithms are discussed for two cascaded MPCs and compared to state of the art controls for wheel selectively drive and steered vehicles.



Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 28.05.2021
Sprache Englisch
Identifikator KITopen-ID: 1000131516
Erschienen in American Control Conference, May 24-28, 2021
Veranstaltung Annual American Control Conference (ACC 2021), New Orleans, LA, USA, 26.05.2021 – 28.05.2021
Verlag Institute of Electrical and Electronics Engineers (IEEE)
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