Safety-Enhanced Formation Maneuver Control for Electric Vehicle With Edge-Weighted Topology and Reinforcement Learning Strategy

Electric vehicle fleet (EVF) provides a promising solution for complex missions, while excessive fleet members and obstacle-rich mission environments increase collision probability, thereby threatening system security. Within this context, this article investigates a collision-free formation maneuver control strategy for EVF. Specifically, an edge-weighted Laplacian matrix (EWLM) is developed to evaluate collision risk and ensure intervehicle safety distances. Through obstacle detection, virtual nodes are identified and incorporated into the EWLM. This modification endows each member with obstacle-escaping capabilities in a similar manner. Meanwhile, for inherent hydrodynamic effects and external disturbances, a reinforcement learning echo state network (RL-ESN) is proposed to match uncertainties. Compared with ESN trained by conventional method, RL-ESN provides improved weight convergence rates and more precise uncertainty matching. For convincing, simulation results are presented to demonstrate the superiority of the collision-free control scheme.