Robust Nonlinear Data-Driven Predictive Control for Mixed Vehicle Platoons via Koopman Operator and Reachability Analysis

TL;DR

This paper introduces a robust nonlinear predictive control framework for mixed vehicle platoons that leverages Koopman operator theory and reachability analysis to ensure safety and optimality under uncertain and adverse conditions.

Contribution

The paper develops a novel data-driven control method combining Koopman-based modeling with reachable set analysis for robust nonlinear vehicle platoon control.

Findings

Outperforms baseline methods in tracking accuracy.

Maintains safety under noise, disturbances, and attacks.

Effective in real-world uncertain scenarios.

Abstract

Mixed vehicle platoons, comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), hold significant potential for enhancing traffic performance. However, most existing control strategies assume linear system dynamics and often ignore the impact of adverse factors such as noise, disturbances, and attacks, which are inherent to real-world scenarios. To address these limitations, we propose a Robust Nonlinear Data-Driven Predictive Control (RNDDPC) framework that ensures safe and optimal control under uncertain and adverse conditions. By utilizing Koopman operator theory, we map the system's nonlinear dynamics into a higher-dimensional space, constructing a Koopman-based model that approximates the behavior of the original nonlinear system. To mitigate modeling errors associated with this predictor, we introduce a data-driven reachable set analysis technique that performs secondary learning using matrix zonotope sets, generating a reachable set predictor for over-approximation of the future states of the underlying system. Then, we formulate the RNDDPC optimization problem and solve it in a receding horizon manner for robust control inputs. Extensive simulations demonstrate that the proposed framework significantly outperforms baseline methods in tracking performance under noise, disturbances, and attacks.