Abstract
The increased complexity of regulatory objects formalization significantly increases the requirements for control systems including intelligent ones. The development of control systems with artificial intelligence is significantly limited by the standard automation tools resources. To separate intelligent systems with different structures, a classification is proposed according to the algorithm of the nested cascade, in particular, regulators with classical elements, elementary modules and fuzzy variables that expand the basic term set. The paper shows methods for increasing the system intelligence with fuzzy logic taking into account the possibility of their separation modularly on common industrial controllers. Increasing the intellectual properties of automatic control systems is possible not only by replacing existing classical regulators, but also by integrating intelligent modules with an existing system with decision-making algorithms implementation. Depending on the technological process characteristics, possible options for the synthesis of multi-stage control systems are considered: with a heterogeneous structure including classical elementary links; a homogeneous structure with a set of similar modules, which differ in a single parameter; as well as a fuzzy controller model with various fuzzy variables in the basic term set. The features of multi-stage fuzzy control systems formation for technological processes are revealed depending on the requirements for the regulatory system and the object properties characterized by uniqueness. Modularity and hierarchy in building multi-stage, non-clear process control system models make it possible to reduce the order of the production rules base and, as a result, to reduce the processing time of the fuzzy inference mechanism.
Keywords
multistage fuzzy controller, structural-parametric synthesis, homogeneous and heterogeneous structure, extension of the basic term set.
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