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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Dynamic event-triggering-based distributed model predictive control of heterogeneous connected vehicle platoon under

Hao Zeng¹, Zehua Ye¹, Dan Zhang¹

¹Research Center of Automation and Artificial Intelligence, Zhejiang University of Technology, Hangzhou, 310023, PR China.

ISA Transactions

|July 21, 2024

Summary

This study introduces a robust distributed model predictive control (DMPC) for connected vehicle platoons (CVP) facing denial-of-service (DoS) attacks. The proposed method ensures stable platoon control despite communication disruptions and external disturbances.

Keywords:

DETM DMPC DoS attacks Heterogeneous CVP ISPS

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Area of Science:

Control Systems Engineering
Networked Systems
Cybersecurity

Background:

Connected vehicle platoons (CVP) face communication vulnerabilities, particularly from denial-of-service (DoS) attacks.
Distributed model predictive control (DMPC) is a promising control strategy for CVPs but requires robust mechanisms against disruptions.
Existing control methods may not adequately address the combined challenges of heterogeneous platoons, DoS attacks, and communication asymmetry.

Purpose of the Study:

To develop a DMPC strategy for heterogeneous CVPs resilient to DoS attacks.
To enhance communication reliability and reduce computational load in CVP control systems.
To ensure the stability and feasibility of the control system under adversarial conditions.

Main Methods:

A dynamic event-triggering mechanism (DETM) was designed to optimize communication and computation.
A packet replenishment mechanism was implemented to maintain information integrity during DoS attacks.
Robustness constraints were incorporated into the DMPC algorithm to handle external disturbances.

Main Results:

The proposed DMPC algorithm demonstrated recursive feasibility.
Input-to-state practical stability (ISPS) was proven for the CVP control system.
Simulation results verified the effectiveness and superiority of the developed algorithm compared to existing methods.

Conclusions:

The novel DMPC approach effectively secures heterogeneous CVPs against DoS attacks.
The integrated DETM and packet replenishment mechanism enhance system resilience and efficiency.
The study confirms the practical applicability of the proposed control strategy for autonomous vehicle systems.