Biomaterial Database

Stabilization via Event-Triggered Impulsive Control With Constraints for Switched Stochastic Systems. 2022

Bin Liu, and Zhijie Sun, and Ming Li, and Dong-Nan Liu

This article studies the event-triggered impulsive control (ETIC) with constraints for the stabilization of switched stochastic systems (SSSs). An ETIC scheme with constraints is proposed for SSS by designing two levels of events via three indices: 1) a threshold value; 2) a control-free index; and 3) a check period. It is also constrained via a constraint index. Based on the activation probabilities and transition probabilities of subsystems, the stabilizations in terms of the p th moment exponential stability and almost exponential stability are achieved, respectively, by the ETIC with constraints. Moreover, based on the scheme of ETIC with constraints, sampling-based ETIC and random ETIC are proposed, respectively. The stabilization conditions via sampling-based ETIC and random ETIC are also derived. It is shown that the ETIC with constraints is non-Zeno and robust with respect to time delays and can achieve lower impulse frequency than the classic time-based impulsive control and recent ETIC schemes. Finally, two examples are presented to demonstrate the effectiveness of the ETIC with constraints.

UI	MeSH Term	Description	Entries
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
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