The development of means and systems of industrial automation, taking place along with the widespread use of modern information technologies, makes it possible to identify trends characteristic of this field of science and technology, and to predict the directions by which the most important changes will occur in the near future. It is shown that the main trend is a constant increase in the level of built-in artificial intelligence in control systems. From the standpoint of the most demanded and relevant areas of research and development, the driving forces and trends in the development and improvement of industrial automation systems are analyzed.

This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach.

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The problem of estimating unknown input effects in control systems based on the methods of the theory of optimal dynamic filtering and the principle of expansion of mathematical models is considered. Equations of dynamics and observations of an extended dynamical system are obtained. Algorithms for estimating input signals based on regularization and singular expansion methods are given. The above estimation algorithms provide a certain roughness of the filter parameters to various violations of the conditions of model problems, i.e. are not very sensitive to changes in the a priori data.

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