Distributed Stochastic Model Predictive Control With Taguchi’s Robustness for Vehicle Platooning

Abstract

Vehicle platooning for highway driving has many benefits, such as lowering fuel consumption, improving traffic safety, and reducing traffic congestion. However, its performance could be undermined due to uncertainty. This work proposes a new control method that combines distributed stochastic model predictive control 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). Taguchi’s robustness is achieved by introducing a variation term in the control objective to bring a trade-off between mean performance and its variation. TR-DSMPC propagates uncertainty via an approximation method: First-Order Second Moment, which is far more efficient than Monte Carlo-based methods. The uncertainty is considered from two perspectives, time-independent uncertainty by random variables and time-dependent uncertainty by stochastic processes. We compare the proposed method with two other MPC-based methods in terms of safety (spacing error) and efficiency (relative velocity). The results indicate that our proposed method can effectively reduce the performance variation and maintain the mean performance.