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Recently, minimum entropy control methods has been successfully used as an information theoretic criterion for non-Gaussian stochastic systems. In this paper, a new single neuron control strategy for nonlinear and non-Gaussian unknown stochastic systems has been proposed in the framework of information theory. Firstly, instead of minimum error entropy criterion, the survival information potential...
This paper proposes a method to stabilize the origins of deterministic dynamical systems by state feedback control laws with Wiener processes. First, we obtain Ito-type stochastic dynamical systems by randomizing ordinary differential equation systems. Second, we design the diffusion coefficients. Then, we obtain Sontag-type global asymptotically stabilizers based on the stochastic control Lyapunov...
A novel delay system approach to network-based control is presented in this paper. The induced delays in networked control systems (NCS) with random access networks are usually non-Gaussian random variables, in addition, the noises in NCSs are probably non-Gaussian random variables as well. Consequently, the controller is designed based on the recently developed stochastic distribution control strategies...
In this paper, a new optimal fault-detection (FD) problem is addressed for a class of non-Gaussian stochastic systems called stochastic distribution systems (SDSs). For an SDS, the available information for the FD system may be the measured output probability density function. A sufficient existence condition of guaranteed cost filters is presented by constructing an augmented Lyapunov functional...
Stochastic distribution control systems aims at the controller design so as to realize a shape control of the distributions of certain random variables in the process. Once the probability density functions (PDFs) of these variables are used to describe their distributions, the control task is to obtain control signals so that the output PFDs of the system are made to follow their target PDFs. In...
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