Distributed Data-Enabled Predictive Control For Vehicle Platoon With Model Uncertainties

To mitigate the impact of model uncertainties and the nonlinear dynamic characteristics of actuators on the control of heterogeneous vehicle platoon, this study proposes a distributed data-enabled predictive control (DeePC) strategy. This strategy can effectively guide unknown dynamic systems to move along expected trajectories while satisfying system constraints. Firstly, a non-parametric model of the vehicle system, which takes into account the nonlinear dynamic characteristics of the actuator, is established using input-output (I/O) vehicle trajectories. Then, the regularized DeePC and the distributed control are integrated and applied to the data-driven control of vehicle platoon based on the non-parametric model. Furthermore, a local cost function, considering other vehicles information and constraints, is designed to optimize the control problem of vehicle platoon. Finally, the simulation results verify the effectiveness of the distributed data-enabled predictive control strategy in vehicle platoon control. Compared with distributed nonlinear model predictive control, the proposed distributed data-enabled predictive control exhibits stronger robustness.