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Stochastic distribution control (SDC) for non-Gaussian system is a mathematically complicated yet practical problem to solve. The most recent solution involves a radial basis function neural network (RBFNN) framework to approximate non-Gaussian output probability density function (PDF). The dynamic weights of such neural network are controlled within each batch of ILC, using a dedicated adaptive controller...
This paper presents model reference adaptive control (MRAC) approach to control the shape of output distribution in non-Gaussian stochastic systems. The method is based on Iterative Learning Control (ILC) and employs a neural network framework for controller design. The output probability density function (PDF) tracking problem is first reduced to dynamic neural network (NN) weight control. It is...
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