ADAPTATION OF A PREDICATE MODEL IN CONTROL PROBLEMS OF NONSTATIONARY STATIC OBJECTS

When controlling static objects at the optimization level, pattern recognition methods are used that allow partitioning the factor space into elementary subdomains in the form of n-dimensional hyperparallelepipeds. One of the main elements of the control structure for this approach to control is the adaptation algorithm, that makes it possible to refine the description of a static object under nonstationarity conditions. Repeated use of the adaptation algorithm to refine the model leads to an unjustified complication of its logical structure and the accumulation of information that has lost its relevance. The paper proposes a method for minimizing the description of images of technological situations, that makes it possible to overcome the indicated disadvantages. The method is based on the property of invariance of the number of parameters defining the hyperparallelepiped to the size of the described area in the factor space. This made it possible to identify significant boundary sub-areas in the description of the image of technological situations and, by their subsequent combination in the direction of the feature axes, to select a description with a minimum number of sub-areas. When performing these operations, “outdated” information is removed and the logical structure of a static control object is simplified as much as possible. The paper shows the possibility of implementing an algorithm for minimizing the description of images on the basis of α-algebra, that makes it possible to integrate its control structures using relational data models. The effectiveness of the proposed algorithm is confirmed by computational experiments in the control of the process of lump crushing for the conditions of a mining and processing plant.