A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars
<|reference_start|>A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars: This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be real-time affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.<|reference_end|>
@article{tiriolo2024a,
    title={A Feedback Linearized Model Predictive Control Strategy for
  Input-Constrained Self-Driving Cars},
    author={Cristian Tiriolo and Walter Lucia},
    journal={arXiv preprint arXiv:2405.01753},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01753},
    primaryClass={eess.SY cs.SY math.OC}
}