Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platooning Under Uncertainty

Vehicle platooning for connected and automated vehicles (CAVs) has many potential benefits, such as lowering fuel consumption, improving traffic safety, and reducing traffic congestion. However, challenges remain toward safe and efficient vehicle platooning since its performance could be degraded due to uncertainty from vehicle dynamics, environmental disturbances, and communication delays, among others. In this talk, I will introduce one recent work on a new control method that combines distributed stochastic model predictive control (DSMPC) with Taguchi’s robustness (TR-DSMPC) for vehicle platooning. The proposed method inherits the advantages of both Taguchi’s robustness (maximizing the mean performance and minimizing the performance variation due to uncertainty) and stochastic model predictive control (ensuring a specific reliability level). The proposed method was compared with two other MPC-based methods in terms of safety (spacing error) and efficiency (relative velocity). The results indicate that the proposed method can effectively reduce the performance variation and maintain the mean performance compared to other methods.