The leader–follower dynamic event-triggered controller is proposed for multiagent systems (MASs) subject to input time delay under time-varying coupling control gain. To begin with, the Halanay inequality, which is referred to as a delayed differential inequality, is used to deal with the input time-delay leader–follower problem. Second, a time-varying event-triggered control protocol with a dynamic triggering mechanism is adopted to save the communication resources with the satisfaction of state synchronization among agents at an exponential convergence rate. Additionally, a novel dynamic triggering mechanism that does not require continuous communication information among agents is further generated with more trigger times than before. Both dynamic triggering mechanisms show better superiority in excluding Zeno behavior and saving communication resources due to the embedding of a positive dynamic parameter in the triggering mechanisms. Finally, a simulation example and the F16 aircraft systems are presented to demonstrate the availability of the theoretical results.

A novel algorithm for automatic behavioral model topology formulation (MTF) is described in this paper. Several new terms are defined to support this formulation approach. The algorithms for determining the controllability and equivalence of branches are developed. The identification of differential pairs and current mirror structures is implemented automatically. Some other algorithms identify a subset of nodes in circuits that are extracted and modeled, while the other nodes will be collapsed. The MTF algorithm then is applied to construct a new signal-path graph, i.e., forming an equivalent circuit that has the ability to represent the behavior of the original circuit in a much-simplified form. This algorithm is implemented in a fully automated modeling tool, ASCEND, which starts from the netlist description of a circuit and generates a differential-algebraic-equation-based model. These models are able to represent static and dynamic behaviors with excellent accuracy and significant simulation speedup. The details of the MTF algorithm are described. Examples of applying the MTF algorithm for behavioral modeling are given.