A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars

TL;DR

This paper introduces a real-time, affordable dual-mode Model Predictive Control strategy for self-driving cars that ensures constraint satisfaction and improved tracking performance, validated through laboratory experiments.

Contribution

It develops a novel input-constrained MPC approach based on feedback linearization, ensuring real-time feasibility and robustness for self-driving car trajectory tracking.

Findings

Outperforms two alternative control schemes in tracking accuracy

Ensures recursive feasibility and constraint satisfaction

Validated through laboratory experiments on Quanser Qcar

Abstract

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.