Reinforcement Learning Enabed Vehicular Optimal Platoon Control under Uncertainties and Cyberattacks

Abstract This paper proposes a reinforcement learning (RL) based optimal platoon control framework for autonomous vehicles (AVs) subject to uncertainties and cyberattacks. Within the optimized backstepping design, an actor-critic-identifier architecture is employed to improve platoon control performance. A cyberattack compensation mechanism and a fuzzy logic based composite observer are integrated to reconstruct reliable vehicle information and estimate system uncertainties under compromised measurements, including the internal vehicular system states, uncertainties, and cyberattacks. In addition, a second-order tracking differentiator is introduced to smooth transient responses and substantially reduce the computational burden associated with differentiating the virtual controllers generated during the backstepping procedure. Based on the Lyapunov function based theory, the stability of the entire vehicle platoon is established, and all relevant errors in the multi-vehicle system are proven to be semiglobally uniformly ultimately bounded (SGUUB). Finally, numerical simulations of a multi-vehicle platooning system validate the effectiveness of the proposed framework and demonstrate its robustness against the simultaneous presence of uncertainties and cyberattacks. Moreover, a comparative study with a non-optimal control algorithm is conducted, highlighting the superior tracking performance and transient response of the proposed RL-enabled optimal control framework.