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 compe...
Zi-Ming Wang, K. H. Johansson, Apostolos I. Rikos· Journal of Intelligent and C...· 0 citations
This paper presents an adaptive hybrid event-triggered switched formation control strategy for the multi-vehicle systems containing autonomous vehicles (AVs) to address uncertainties in both longitudinal and lateral dynamics. The proposed framework enables safe and efficient maneuvering of AV formations in complex traf...
Zi-Ming Wang, Shi-Yuan Piao, Xin Wang et al.· IEEE Systems, Man, and Cyber...· 0 citations
This work proposes a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties that uses the actor-critic-identifier structure and provides a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking be...
Ziming Wang, Bingbing Li, Karl H. Johansson et al.· arXiv.org· 0 citations
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