Optimized Adaptive Asymptotic Control for Leaderless Multi-agent Systems under Deception Attacks

This article concentrate on optimized adaptive consensus control problem for the nonlinear multi-agent systems subjected to deception attacks. To mitigate the influence of false data on the system, the transformed tracking error is formulated under the backstepping technique, and the optimized consensus control strategy incorporating Nussbaum technique is utilized to eliminate the destabilizing effects of time-varying gains in the attack. The proposed critic-actor structured reinforcement learning optimal algorithm executes control actions via the actor neural network and evaluates system performance through the critic neural network, enabling the closed-loop system to achieve optimized tracking control performance. Based on the Lyapunov stability method, it is demonstrated that all signals bounded within the closed-loop system, ensuring the achievement of asymptotic output consensus. To illustrate the efficacy of the proposed control approach, some simulation results are finally presented.