Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study

TL;DR

This paper compares different black-box optimization methods for tuning a compensator in Twin-in-the-Loop vehicle control, demonstrating VRFT's efficiency and SMGO's reduced computational cost in a yaw-rate tracking case study.

Contribution

It introduces a comparative analysis of Bayesian Optimization, SMGO, and VRFT for calibrating control compensators in TiL-C, highlighting VRFT's one-shot tuning advantage.

Findings

VRFT achieves effective tuning after a single iteration.

SMGO reduces computational effort compared to Bayesian Optimization.

Global optimizers require 10-15 iterations for refinement.

Abstract

Given the urgent need of simplifying the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run on-board to compute a nominal control action in run-time and an additional block C_delta is used to compensate for the mismatch between the simulator and the real vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of C_delta. More specifically, we compare the originally proposed Bayesian Optimization (BO) approach with the recently developed Set Membership Global Optimization (SMGO) and Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study -- the yaw-rate tracking problem -- so as to further prove the TiL-C effctiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to significantly reduce the computational effort required by BO.