Human-in-The-Loop Prescribed Performance Optimal Control for Multiagent Systems Under DoS Attacks: An Error Reconstruction Mechanism

This paper investigates the human-in-the-loop (HiTL) prescribed performance optimal consensus control problem for nonlinear multiagent systems (MASs) under denial-ofservice (DoS) attacks, where DoS attacks are viewed as attacks against inter-agent communication. First, to respond to any emergencies and guarantee the safety of MASs, the MASs are monitored by a human operator sending command signals to the non-autonomous leader. Then, to address the problem of consensus error leaping in attack switching instants, a consensus error reconstruction mechanism based on piecewise differentiable function is proposed. In conjunction with this mechanism, a performance index function involving transformed error and control input is developed to achieve both the optimal consensus and prescribed performance. Subsequently, the reinforcement learning method is employed to learn the solution to Hamilton-Jacobian-Bellman equation, in which the fuzzy logic systems are employed to implement this method. Finally, the simulation verifies the effectiveness of the proposed control scheme.