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Complex stochastic systems require the control of their stochastic distributions (i.e., the shape of their output probability density functions (PDFs)). This paper will address both modelling and control of such systems and will consist of a brief survey of the recent developments and the description of a detailed design procedure on an iterative learning-based output PDF control algorithm. In this...
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...
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...
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...
Complex stochastic systems require the control of their stochastic distributions. This keynote paper addresses both modelling and control of such systems and consists of the following aspects: 1) neural network based modelling of the stochastic distribution systems; 2) control framework for the stochastic profile control of the systems; 3) iterative learning of the space variables so as to achieve...
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