Stochastic hybrid systems with renewal transitions: moment analysis with application to networked control systems with delays (Q2840145)
From MaRDI portal
| This is the item page for this Wikibase entity, intended for internal use and editing purposes. Please use this page instead for the normal view: Stochastic hybrid systems with renewal transitions: moment analysis with application to networked control systems with delays |
scientific article; zbMATH DE number 6188903
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Stochastic hybrid systems with renewal transitions: moment analysis with application to networked control systems with delays |
scientific article; zbMATH DE number 6188903 |
Statements
17 July 2013
0 references
networked control systems
0 references
Volterra integral equations
0 references
semi-Markov processes
0 references
processes in a random environment
0 references
Stochastic hybrid systems with renewal transitions: moment analysis with application to networked control systems with delays (English)
0 references
SHS (stochastic hybrid systems) with \(n\)-dimensional real state space ``combine continuous dynamics and discrete logic'' in the following way: There is a finite set of modes the system may attain, changing modes depend deterministically on the present mode and state of the system at transition times. Inter-transition times are random, depending on the actual mode and are determined by a set of competing clocks, the minimal clock time determines the next transition instant.NEWLINENEWLINEThe development of the system between transition instants is determined by a linear (deterministic) differential equation, the differential operator is determined by the actual mode. Whenever a transition instant occurs the state of the system may be reset, depending again deterministically on the mode just before transition and the (number of the) clock which triggered the transition instant.NEWLINENEWLINEThe authors determine a set of Volterra equations to compute multidimensional mixed moments of the marginal state variables and provide numerical methods to solve the equations and discuss how to estimate from the obtained moments at time \(t\) the state distribution at time \(t\).NEWLINENEWLINEFurthermore, Lyapunov exponents of higher order are derived via root finding for a spectral equation.NEWLINENEWLINEAs an example the authors consider feedback control in a network control problem.
0 references