Dynamic consensus control of linear multi-agent systems: Unmanned ground vehicle platooning

This article presents the dynamic consensus control framework for linear multi-agent systems (MAS) using Distributed Model Predictive Control (DMPC) as the primary strategy to achieve both static and dynamic consensus in vehicle platoons. The proposed approach incorporates state and control constraints to ensure feasible and safe operations within desired limits, making it particularly suitable for practical applications in coordinated vehicle control. By employing MPC, the system anticipates future states, enabling predictive adjustments that enhance overall system stability and consensus accuracy. Theoretical analysis establishes the conditions under which consensus is achieved, while MATLAB simulations validate the effectiveness of the control scheme in managing the desired platoon behaviors under various dynamic scenarios. The results demonstrate that the MPC-based approach successfully achieves consensus among agents, providing robust performance despite the inherent system constraints. Comparison of the characteristics with existing control schemes demonstrate the feasibility of the proposed DMPC design, while maintaining comparable performance.