This article describes the process of multicriteria optimization of a complex industrial control object using Pareto efficiency. The object is being decomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network approximates key sensors data to identify the model of the controllable object and optimize it.
Authors review methods of determining a plant’s mathematical model. Then, they show a numerical method of pulse automatic control systems’ (ACS) identification, focused on computer technology, the interpolation procedure and iterative methods of approximation to the desired solution. The basis of the approach is the method of inverse problems of dynamics and real interpolation method for calculating the linearized dynamical systems. An algorithm and the mobile device designed for the identification of facilities management in operational conditions are proposed. There is results’ application in the conclusion.
This paper describes modernisation of a production process using multicriteria optimisation logic and Augmented Reality enabled hardware. The process is being decomposed and viewed as a hierarchy of embedded sub-processes. Key performance indicators are extracted and evaluated, thus allowing to systematise sources of influence. After the multicriteria optimisation pass is finished, a new intelligent control system containing both soft-and hardware bits is introduced. Its designation is to manage technological process more effectively, lower human resources involvement and increase the process' overall ergonomics.
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