Nonfragile Finite-Time Stabilization for Discrete Mean-Field Stochastic Systems
Tianliang Zhang, Feiqi Deng, Peng Shi
Abstract
In this article, the problem of nonfragile finite-time stabilization for linear discrete mean-field stochastic systems is studied. The uncertain characteristics in control parameters are assumed to be random satisfying the Bernoulli distribution. A new approach called the “state-transition matrix method” is introduced and some necessary and sufficient conditions are derived to solve the underlying stabilization problem. The Lyapunov theorem based on the state-transition matrix also makes a contribution to the discrete finite-time control theory. One practical example is provided to validate the effectiveness of the newly proposed control strategy.
Topics & Concepts
Control theory (sociology)Discrete time and continuous timeStochastic processMathematicsApplied mathematicsComputer scienceControl (management)StatisticsArtificial intelligenceStability and Control of Uncertain SystemsAdaptive Control of Nonlinear SystemsFault Detection and Control Systems