Priority-Driven Constraints Softening in Safe MPC for Perturbed Systems
Journal article, 2025
This letter presents a safe model predictive control framework designed to guarantee the satisfaction of hard safety constraints, for perturbed dynamical systems. Safety is guaranteed by softening the constraints selected on a priority basis from a subset of constraints defined by the designer. Since such an online selection is the result of an auxiliary optimization problem, its computational overhead is alleviated by off-line learning its approximated solution, rather than solving it exactly online. Simulation results, obtained from an automated driving application, show that the proposed approach provides guarantees of collision-avoidance hard constraints despite the unpredicted behaviors of the surrounding environment.
Softening
Model predictive control
Simulation
Control systems
Predictive control
Autonomous vehicles
Dynamical systems
constrained control
Optimization
Computational complexity
uncertain systems
Uncertain systems
Safety
safety critical
Author
Yingshuai Quan
Chalmers, Electrical Engineering, Systems and control
Mohammad Jeddi
University of Modena and Reggio Emilia
Polytechnic University of Bari
Francesco Prignoli
University of Modena and Reggio Emilia
Paolo Falcone
Chalmers, Electrical Engineering, Systems and control
IEEE Control Systems Letters
24751456 (eISSN)
Vol. 9 1069-10745G for Connected Autonomous Vehicles in Complex Urban Environments
VINNOVA (2018-05005), 2019-04-01 -- 2023-03-31.
Subject Categories (SSIF 2025)
Computer Sciences
Computer Systems
Control Engineering
DOI
10.1109/LCSYS.2025.3580494