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A new approach of fault detection and diagnosis (FDD) for general stochastic systems in discrete-time is studied. Our work on this problem is motivated by the fact that most of the nonlinear control laws are implemented as digital controllers in reality. Different from the formulation of classical FDD problem, it is supposed that the measured information for the FDD is the probability density functions...
In this paper, we propose to find upper/lower bounds for different measures that characterize the reachability problem defined in the context of stochastic hybrid systems, using the theory of large deviations. For stochastic hybrid processes, criteria for large deviation results are given using properties of their infinitesimal generators. This represents just the first step towards applying large...
This semi-plenary paper presents a brief and selected survey on the advances on stochastic distribution control, where the purpose of the controller design is to control the shape of output probability density functions (pdf) of non-Gaussian and general stochastic systems. This research was motivated through the requirement of distribution shape control of a number of practical systems in 1996. Following...
A new type of data-driven control framework for non-Gaussian stochastic systems is established in this paper. Different from the traditional feedback style, the driven information for tracking problem is the statistic information set (SIS) of the output rather than the output value. The set of statistical information (including the moments and the entropy) or probability density functions (PDFs) of...
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...
In this paper, an Iterative Learning Control (ILC) scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the stochastic system is not necessarily Gaussian. A Radial Basis Function Neural Network (RBFNN) has been employed...
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