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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...
In this paper a new method for adaptive control of the general stochastic systems has been proposed. The method applies the minimum entropy control scheme to decrease the closed loop randomness of the output in an iterative learning control (ILC) basis. Both modeling and control of the plant are performed using dynamic neural networks. For this purpose, the whole control horizon is divided into a...
In this paper, a minimum entropy control approach of closed-loop tracking error is studied for linear dynamic control systems subjected to stochastic disturbances. The basic idea is to construct a feedback control scheme with the guaranteed closed loop stability using a real updating Yula parameterization formula in every updated subinterval, where the entropy of the closed loop tracking error are...
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