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In this paper, a new algorithm for an adaptive Proportional-Integrator (PI)controller for nonlinear systems subjected to stochastic non-Gaussian disturbance is studied. The minimum entropy control is applied to decrease the closed-loop tracking error under an iterative learning control (ILC) basis. The key issue here is to divide the control horizon into a number of equally time-domain intervals called...
Minimum variance control is an established method in control of systems corrupted by noise. In these cases, as it is not possible to directly control the actual value of the system variables, one aims to reduce the variations instead. However, when the system noises are non-Gaussian, this approach fails because non-Gaussian noise cannot be characterised by simple measures such as variance. In these...
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...
In this paper, a new method for the control of the shape of the conditional output probability density function (pdf) for general nonlinear dynamic stochastic systems is presented using two-step neural networks (NNs). Following the square-root B-spline NN approximation to the measured output pdf, the problem is transferred into the tracking of dynamic weights. Different from the previous related works,...
In this paper, a fixed-structure iterative learning control (ILC) control design is presented for the tracking control of the output probability density functions (PDF) in general stochastic systems with non-Gaussian variables. The approximation of the output PDF is firstly realized using a radial basis function neural network (RBFNN). Then the control horizon is divided to certain intervals called...
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