Biomaterial Database

Quasi-synchronization of drive-response systems with parameter mismatch via event-triggered impulsive control. 2023

Huannan Zheng, and Nanxiang Yu, and Wei Zhu

School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Key Lab of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.

In this paper, an event-triggered impulsive control method is proposed to investigate the quasi-synchronization of drive-response systems with parameter mismatch, which integrates the event-triggered control and impulsive control together. The impulsive instants are event-triggered and determined by a certain state-dependent triggering law. Sufficient conditions for achieving quasi-synchronization are achieved. The synchronization error is shown to be no more than a nonzero bound. Furthermore, Zeno-behavior of impulsive instants is excluded. Finally, a numerical example is presented to verify the validity of the theoretical results.

UI	MeSH Term	Description	Entries
D013997	Time Factors	Elements of limited time intervals, contributing to particular results or situations.	Time Series,Factor, Time,Time Factor
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron