Passivity‐based stochastic sampled‐data control of Markovian jump systems via looped‐functional approach

Abstract

In this article, passivity-based sampled-data control for Markovian jump systems (MJSs) with stochastic sampling is conducted by virtue of the looped-functional approach. First, based on the input delay approach, the MJS under stochastic sampled-data control is modeled as a time-varying continuous-time system with a random variable. Second, a time-dependent looped-functional is developed, which only requires its positive definiteness at the sampling instants and takes full use of the state information. Moreover, a free-matrix-based integral inequality is used to derive some less conservative passivity conditions expressed in terms of linear matrix inequality (LMIs). Meanwhile, a design algorithm for the mode-dependent stochastic sampled-data controller is provided. A numerical example is given to demonstrate the validity and superiority of the proposed analysis method.

Finite-time energy-to-peak control for Markov jump systems with pure time delays

“…We assume the transition rate matrix Π = [ left left − 0 . 3 left 0 . 3 left1 0 . 7 left − 0 . 7 ] . Because the subsystems 2.1 and 2.2 both have no current state, theorems in the references 31,43–45 cannot deal with this MJS. Assume this MJS is under zero initial condition, and a disturbance signal (see Figure 1: EI Centro 1940 earthquake excitation), is introduced to this MJS.…”

Stability analysis of hybrid stochastic delay differential equations with asynchronous switching and discrete observations

“…Sampled‐data control differs from general control in that the controller is updated not at every moment but at each sampling point $$$ {t}_k= k\delta, \kern3.0235pt k=0,1,2,\dots $$$, where $$$ \delta >0 $$$ is the sampling interval. Accordingly, sampled‐data control has received more and more attention and there are fruitful and mature results on analysis and synthesis of sampled‐data systems; see [23–29].…”

Fuzzy sampled‐data control of PMSG‐based wind turbine systems with random occurring uncertainties

“…Energy consumption can be reduced by increasing the sampling interval with this sampling approach, which is a certainty. Concerns have been raised about the use of sampling data for control purposes 8‐11 . The authors in Reference 12 have proposed a decentralized adaptive fuzzy SDC based on an output feedback control strategy to obtain the semiglobal uniformity and ultimately boundedness of large‐scale interconnected systems.…”