Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles

TL;DR

This paper introduces a robust iterative learning framework for collaborative road profile estimation and active suspension control in connected vehicles, improving accuracy and robustness through iterative refinement and accommodating vehicle heterogeneity.

Contribution

It proposes a novel robust iterative learning approach for collaborative road profile estimation and suspension control, adaptable as an add-on to existing systems.

Findings

Enhanced estimation accuracy demonstrated in simulations

Robustness against vehicle heterogeneity validated

Framework effectively improves suspension control performance

Abstract

This paper presents the development of a new collaborative road profile estimation and active suspension control framework in connected vehicles, where participating vehicles iteratively refine the road profile estimation and enhance suspension control performance through an iterative learning scheme. Specifically, we develop a robust iterative learning approach to tackle the heterogeneity and model uncertainties in participating vehicles, which are important for practical implementations. In addition, the framework can be adopted as an add-on system to augment existing suspension control schemes. Comprehensive numerical studies are performed to evaluate and validate the proposed framework.