Adaptive Fault-Tolerant Optimized Platoon Cloud Tracking Control for Heterogeneous Vehicles via Dual Learning Mechanism

This work investigates an adaptive fault-tolerant cloud tracking control scheme for heterogeneous vehicular platoon systems (HVPSs) based on a dual learning mechanism (DLM). A human-vehicle-road-cloud traffic scenario is built, in which the cloud control platform operates HVPSs by sending the command signal to a non-autonomous leader vehicle, which generates the required trajectory. Moreover, a DLM consisting of an improved reinforcement learning mechanism and a composite learning mechanism is proposed to optimize the control performance of the entire vehicular platoon. Furthermore, unlike existing fault-tolerant control methods, the proposed DLM can effectively reduce the adverse effects caused by actuator fault. Via the DLM, and cloud control platform, an adaptive fault-tolerant optimized platoon technology is developed to guarantee the bistability. Finally, simulation results certify the effectiveness and superiority of the developed strategy.
Note to Practitioners
—This paper is inspired by the problem of vehicular platoon control, but the proposed approach is not only applicable to transportation systems, but also to a variety of scenarios such as smart grids, supply chain management, and fire rescue. The intelligent transportation system aims to realize V2P, V2V, and V2I. However, there still exist several problems, such as the lack of obstacle avoidance/collision mechanism, the occurrence of platoon actuator fault, and the excessive consumption of vehicle energy, making it difficult to apply in complex traffic. In this paper, the proposed method can effectively solve the above problems, and simulation experiments have shown that this method is feasible and superior. However, vehicular platoon path programming and external network attacks have not yet been considered. To approach the real traffic environment more closely also prompts us to conduct research in the future.