A Robust Scenario MPC Approach for Uncertain Multi-Modal Obstacles
Artikel i vetenskaplig tidskrift, 2021

Motion planning and control algorithms for autonomous vehicles need to be safe, and consider future movements of other road users to ensure collision-free trajectories. In this letter, we present a control scheme based on Model Predictive Control (MPC) with robust constraint satisfaction where the constraint uncertainty, stemming from the road users' behavior, is multimodal. The method combines ideas from tube-based and scenario-based MPC strategies in order to approximate the expected cost and to guarantee robust state and input constraint satisfaction. In particular, we design a feedback policy that is a function of the disturbance mode and allows the controller to take less conservative actions. The effectiveness of the proposed approach is illustrated through two numerical simulations, where we compare it against a standard robust MPC formulation.

Autonomous vehicles

predictive control for nonlinear systems

uncertain systems

Författare

Ivo Batkovic

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Zenuity AB

Ugo Rosolia

California Institute of Technology (Caltech)

Mario Zanon

IMT Alti Studi Lucca

Paolo Falcone

Universita Degli Studi Di Modena E Reggio Emilia

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

IEEE CONTROL SYSTEMS LETTERS

2475-1456 (ISSN)

Vol. 5 3 947-952

Ämneskategorier

Robotteknik och automation

Reglerteknik

Signalbehandling

DOI

10.1109/LCSYS.2020.3006819

Mer information

Senast uppdaterat

2020-10-01