Robust tuning of Twin-in-the-Loop vehicle dynamics controls via randomized optimization

Abstract

The Twin-in-the-Loop (TiL) framework for vehicle dynamics control has been recently introduced with the goal of simplifying the end-of-line-tuning phase and enhancing the controller performance. In TiL schemes, high-fidelity vehicle models are run on-board to compute the nominal control action, while a simple closed-loop compensator takes the model-mismatch into account. In this paper, we discuss the robustness properties of the TiL approach to uncertain working conditions. In particular, we show that, due to the model-free nature of the compensator tuning, randomized tools represent an effective way to guarantee a certain level of robustness to different operating conditions with reasonable confidence levels. Simulation results illustrate the effectiveness of the proposed approach within a braking control case study.