2021
DOI: 10.1109/access.2021.3053352
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Finite-Time Annular Domain Bounded Control of Itô-Type Stochastic Systems With Wiener and Poisson Random Disturbance

Abstract: This paper is concerned with the finite-time annular domain bounded control of Itô-type stochastic systems with wiener and poisson random disturbance. First, utilizing different quadratic function methods, some sufficient conditions for finite-time annular domain bounded-ness (FTADB) of the system are achieved. Second, two finite-time annular domain bounded controllers are skillfully developed to ensure the FTADB of the closed-loop system, of which one is state feedback controller and the other is dynamic outp… Show more

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Cited by 3 publications
(2 citation statements)
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“…Regime-switching jump diffusion processes can be seen as a jump diffusion process in a stochastic environment, where the evolution of the stochastic environment is modeled by continuous-time Markov chain, or more generally, a continuous-state-dependent switching process with a discrete state space [17]. In order to simulate more systems, many scholars have considered and studied stochastic systems [4], [8]- [15], [22], and also studied many properties of the regime-switching jump diffusion system, such as asymptotic stability in probability, finite-time annular domain stability in the sense of expectation and so on.…”
Section: Introductionmentioning
confidence: 99%
“…Regime-switching jump diffusion processes can be seen as a jump diffusion process in a stochastic environment, where the evolution of the stochastic environment is modeled by continuous-time Markov chain, or more generally, a continuous-state-dependent switching process with a discrete state space [17]. In order to simulate more systems, many scholars have considered and studied stochastic systems [4], [8]- [15], [22], and also studied many properties of the regime-switching jump diffusion system, such as asymptotic stability in probability, finite-time annular domain stability in the sense of expectation and so on.…”
Section: Introductionmentioning
confidence: 99%
“…Where, 𝑚 -Grey Level Gaussian Distribution noise, µ -The Mean Value 𝜎 -The Standard Deviation Salt and pepper (Binary Noise): Salt and pepper noise is caused by failed camera sensor cells, synchronization errors during image digitization or transmission, or memory cell failure, among other things[29]. This type of noise is sometimes referred to as impulsive noise, shot noise, or binary noise.…”
mentioning
confidence: 99%