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This paper is concerned with the security consensus control issue for discrete-time multiagent systems (MASs) on the basis of a reinforcement learning (RL) approach. Considering the effects of denial-of-service (DoS) attacks, a novel control protocol is proposed to deal with the H ∞ consensus problem. Firstly, a Q -learning algorithm is put forward under the directed graph, which can obtain the target gain matrices without any system dynamics information. In addition, the obtained gain matrices and Lyapunov function are employed to demonstrate that the MASs can reach security consensus. Moreover, the proof of H ∞ consensus under undirected graphs is derived using the designed Q -learning algorithm. In the end, the simulation experiments are given to verify the correctness of the designed control strategy. [ABSTRACT FROM AUTHOR]

The issue of H ∞ state estimation is addressed for complex networks (CNs) with inner coupling disturbance (ICD) while utilizing adaptive-frequency event-triggered mechanism (AFETM) and multi-channel random access protocol (MRAP). To maintain transmission frequency as much as possible while reducing data transmission, the proposed AFETM dynamically modifies the threshold based on the historical number of transmissions within a defined time interval. Data transmission after AFETM is scheduled by the MRAP, which enables the random selection and transmission of multiple signals, thereby avoiding network conflicts and congestion. The primary objective is to devise a estimator that guarantees the estimation errors meet the prescribed H ∞ performance. The attainment of sufficient conditions to achieve this objective involves the utilization of stochastic analysis. On this basis, the expected estimator gain is determined through the solution of the backward coupled difference equations. Ultimately, the performance of the devised estimator is validated by simulation. [ABSTRACT FROM AUTHOR]