A Robust Scenario MPC Approach for Uncertain Multi-Modal Obstacles
Journal article, 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


Ivo Batkovic

Zenuity AB

Chalmers, Electrical Engineering, Systems and control

Ugo Rosolia

California Institute of Technology (Caltech)

Mario Zanon

IMT School for Advanced Studies

Paolo Falcone

University of Modena and Reggio Emilia

Chalmers, Electrical Engineering, Systems and control

IEEE Control Systems Letters

24751456 (eISSN)

Vol. 5 3 947-952

Subject Categories


Control Engineering

Signal Processing



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