Twin-in-the-Loop observer tuning via gradient-information Bayesian optimization with line search

Control Engineering Practice, vol. 172, pp. 106891

Abstract

High-fidelity Digital Twins (DTs) are standard tools for offline tasks such as virtual prototyping and validation, yet their deployment in real-time control applications remains limited. The Twin-in-the-Loop (TiL) observer architecture addresses this by leveraging a DT as an embedded prediction model to simultaneously estimate the full state of a complex system. However, the use of a black-box DT precludes analytic gradients, turning observer tuning into a challenging high-dimensional, zeroth-order optimization problem. This study addresses this challenge by applying and extending Gradient Information with Bayesian Optimization ( GIBO ), an algorithm designed for high-dimensional black-box local optimization. We introduce GIBO+LS , a novel variant augmented with a one-dimensional line search, which significantly reduces convergence variability and computational time. Furthermore, we integrate a pseudo-random warm-start strategy and an average-cost optimization across multiple datasets to enhance global search capabilities and empirical robustness. Case studies in a vehicular application using real-world data demonstrate that the proposed method reduces median velocity estimation error by 34% over previous TiL tuning methods and by up to 46% over established Kalman-filter benchmarks.

Authors 5

1. Giacomo Delcaro corresponding

   Politecnico di Milano

   Affiliation as printed

   Electronics, Computer Science and Bioengineering Department (DEIB), Politecnico di Milano, Milano, Italy

2. Johanna Menn Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering (DSME), RWTH Aachen University, Aachen, Germany

3. Sergio M. Savaresi

   Politecnico di Milano

   Affiliation as printed

   Electronics, Computer Science and Bioengineering Department (DEIB), Politecnico di Milano, Milano, Italy

4. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering (DSME), RWTH Aachen University, Aachen, Germany

5. Simone Formentin

   Politecnico di Milano

   Affiliation as printed

   Electronics, Computer Science and Bioengineering Department (DEIB), Politecnico di Milano, Milano, Italy