SummaryConsensus problem of multiagent systems with switching jointly connected topologies under sampled‐data control is studied in this article. The main contribution is that the consensus problem for such system is solved without the assumption that the system matrices are stable or critically stable. For this purpose, a time‐varying Lyapunov function method is utilized to describe the state characteristics with switching jointly connected topologies. Based on the time‐varying matrix of Lyapunov function, the “decline” characteristics at the switching instants is derived to compensate the divergence among the agents with disconnected topologies. Utilizing the “decline” characteristics, the overall consensus of such system can be guaranteed in the framework of dwell time. Finally, the effectiveness of the proposed result is illustrated by two numerical examples.

Summary In this paper, the H∞ tracking control of linear discrete‐time systems is studied via reinforcement learning. By defining an improved value function, the tracking game algebraic Riccati equation with a discount factor is obtained, which is solved by iteration learning algorithms. In particular, Q‐learning based on value iteration is presented for H∞ tracking control, which does not require the system model information and the initial allowable control policy. In addition, to improve the practicability of algorithm, the convergence analysis of proposed algorithm with a discount factor is given. Finally, the feasibility of proposed algorithms is verified by simulation examples.

This paper extends the existing results on the robust H∞ filtering problem for singular Markovian jump systems. Firstly, the double variables‐based decoupling principle and the variable substitution principle are proposed, respectively. Secondly, the two principles are employed to formulate a robust H∞ filter design condition, which ensures the filtering error system to be stochastically admissible and meet H∞ performance. Compared with the existing works, this paper fully considers the free structure of introduced slack matrices, which provides extra dimensions in the solution space. It directly leads to the reduction of conservativeness in the filtering solution. The effectiveness of the proposed methods is illustrated by a numerical example.