# Automatic dimensionality reduction of Twin-in-the-Loop Observers

arXiv: https://arxiv.org/abs/2401.10945 · HTML: https://mlsanity.com/paper/2401.10945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning](https://mlsanity.com/paper/2006.06224/) (2020)
- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [A survey of sparse representation: algorithms and applications](https://mlsanity.com/paper/1602.07017/) (2016)
- [Global optimization via inverse distance weighting and radial basis functions](https://mlsanity.com/paper/1906.06498/) (2019)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [Sparse Methods for Automatic Relevance Determination](https://mlsanity.com/paper/2005.08741/) (2020)
- [The Twin-in-the-Loop approach for vehicle dynamics control](https://mlsanity.com/paper/2209.02263/) (2022)
- [Joint vehicle state and parameters estimation via Twin-in-the-Loop observers](https://mlsanity.com/paper/2309.01461/) (2023)
- [Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments](https://mlsanity.com/paper/2204.06259/) (2022)
- [Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study](https://mlsanity.com/paper/2309.02080/) (2023)

## Abstract (arXiv)

Conventional vehicle dynamics estimation methods suffer from the drawback of employing independent, separately calibrated filtering modules for each variable. To address this limitation, a recent proposal introduces a unified Twin-in-the-Loop (TiL) Observer architecture. This architecture replaces the simplified control-oriented vehicle model with a full-fledged vehicle simulator (digital twin), and employs a real-time correction mechanism using a linear time-invariant output error law. Bayesian Optimization is utilized to tune the observer due to the simulator's black-box nature, leading to a high-dimensional optimization problem. This paper focuses on developing a procedure to reduce the observer's complexity by exploring both supervised and unsupervised learning approaches. The effectiveness of these strategies is validated for longitudinal and lateral vehicle dynamics using real-world data.