This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.

This paper deals with prespecified-time bipartite consensus (PTBC) for leaderless and leader-following multi-agent systems (MASs) via intermittent control. A unified framework for realizing prespecified-time (PT) intermittent control is developed and analyzed theoretically. On the basis of the communication network with cooperative-competitive interaction, both PTBC of leaderless MAS and leader-following MAS are considered. And the distributed controllers rather than the centralized controllers are designed to realize PTBC of the corresponding controlled systems respectively. Further, it is shown that the designed controller is uniformly bounded even though the time-varying feedback gain in the designed controller tends to infinity as time tends to the settling time. Finally, some numerical examples on actual circuit systems are represented to substantiate the validity and application of our theoretical results.

In this paper, the quasi-synchronization of a class of nonlinearly-coupled complex networks is studied. Different from the previous works, a distinguishing characteristic of this work is that the coupling matrix of nonlinear coupled networks is asymmetrical. The effect of time-varying delay in dynamical networks is also considered. By utilizing the aperiodically intermittent pinning control technique, some more general sufficient conditions to guarantee global quasi-synchronization are derived. Finally, a numerical simulation is presented to demonstrate the efficiency of the theoretical findings.

In this paper, the impact of the clustering exponent (α) on synchronizability, average shortest path length and navigability of small-world networks generated by one dimensional Kleinberg model is investigated. It could be seen from the analysis that the synchronizability becomes stronger as the clustering exponent decreases. And the navigability achieves peak at the neighborhood of α = 1, as well as the navigability becomes smaller as the clustering exponent increases. Moreover, the average path length of one dimensional Kleinberg small-world network decreases with respect to increasing clustering exponent. And this phenomenon is verified by numerical simulations on a network of Rossler oscillators. Then, it could be deduced from the phenomenon observed that compared with the low probabilities of longer distance of the edge-adding, the high probabilities of shorter distance of the edge-adding could achieve better synchronizability.