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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]

This paper focuses on the design of H ∞ filter for a class of discrete-time networked systems. Given that the network communication resources are becoming limited with the ever-increasing network traffic, an adaptive event-triggered mechanism (AETM) is adopted for the systems to mitigate the pressure of network bandwidth. The considered discrete-time networked systems are envisioned to suffer from both Deception attacks and denial-of-service (DoS) attacks, thereby a novel hybrid cyber attacks model is firstly constructed to integrate the two kinds of attacks. Then, a filtering error system model is established for the discrete-time networked systems under AETM and hybrid cyber attacks. Based on the constructed model, the sufficient conditions that guaranteeing the asymptotic stability and H ∞ performance of the concerned filtering error system are analyzed based on Lyapunov–Krasovskii stability theory. Furthermore, the corresponding parameters of the designed filter are derived by solving a set of linear matrix inequalities (LMIs). The effectiveness of the designed filter is finally demonstrated by conducting a numerical example. [ABSTRACT FROM AUTHOR]