Institute of Control Sciences of the Russian Academy of Sciences, Moscow, Russia:

Z. G. Salikhov, Principal Researcher, Professor, Doctor of Technical Sciences, e-mail: zuf1940@yandex.ru

North Caucasian Institute of Mining and Metallurgy (State Technological University), Vladikavkaz, Russia:
A. L. Rutkovskiy, Professor, Department of Theory and Automation of Metallurgical Processes and Furnaces, Candidate of Technical Sciences, e-mail: rutkowski@mail.ru

Vladikavkaz Branch of the Financial University under the Government of the Russian Federation, Vladikavkaz, Russia:
M. A. Kovaleva, Acting Head of the Department of Corporate Information and Communication Systems, Associate Professor at the Department of Mathematics and Computer Science, Candidate of Technical Sciences, e-mail: Mary_kovaleva@list.ru

This paper describes a method of adaptive identification under uncertainty when one only knows the probable value regions of input and output stochastic variables of an object. The authors describe a solution for estimating the functionals describing the stochastic process. The solution implies a consistent rise in the number of observations and at the same time minimisation of the maximum possible error. A recursive type of minimax estimation technique is presented. As the number of observations rise, the information acquired at the previous calculation stages is retained and utilized in further calculations. The methods and results of this research were applied in theoretical and applied research projects that looked at optimizing the processes of fluidizedbed roasting of zinc concentrates and fuming of middlings in rotary kilns. It is proposed to also apply them to design multi-level systems to control complex metallurgical processes when no control of the stochastic coordinates of the object’s state is possible.

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