Coalitional Distributed Model Predictive Control Strategy for Vehicle Platooning Applications.

Area of Science:

• Control Systems Engineering
• Robotics and Automation
• Transportation Systems

Background:

• Vehicle platooning requires advanced control strategies for safety and efficiency.
• Distributed Model Predictive Control (DMPC) offers decentralized decision-making capabilities.
• Ensuring stability and flexibility in DMPC for dynamic systems like platoons is challenging.

Purpose of the Study:

• To develop and validate a novel Coalitional Distributed Model Predictive Control (C-DMPC) strategy for vehicle platooning.
• To enhance the robustness and flexibility of DMPC in platooning applications.
• To ensure algorithmic stability using terminal constraint regions and robust positively invariant sets.

Main Methods:

• Formulation of a Coalitional Distributed Model Predictive Control (C-DMPC) strategy.
• Implementation of terminal constraint regions with robust positively invariant sets for stability.
• Creation of an invariant table set for enhanced flexibility during initialization.
• Simulation testing with homogeneous and heterogeneous initial conditions in a four-vehicle platoon.

Main Results:

• The C-DMPC strategy successfully formed coalitions between vehicles when local feasibility was compromised.
• The algorithm demonstrated robustness across different initial conditions.
• Simulations confirmed the effectiveness of the developed C-DMPC for vehicle platooning.

Conclusions:

• The proposed C-DMPC method is a viable and effective strategy for vehicle platooning applications.
• The algorithm's ability to form coalitions enhances its adaptability and reliability.
• The study highlights the robustness and practical utility of the C-DMPC approach in dynamic platooning scenarios.