Network fuzzy situational precedent models of control systems for complex technical objects

The control of complex technical objects is often hampered by the lack of information necessary to build probabilistic models of the control process. An alternative approach is based on models in which elements of fuzzy set theory are used to identify an effective control strategy from the point of view of the selected criterion. The disadvantages of fuzzy situational models include the subjective nature due to the expert data used for their construction, as well as the inability to distinguish in sufficient detail the reactions of an object to the same control actions, which is most clearly manifested in complex technical objects, the functioning of which is influenced by many situational factors. The paper proposes an approach to the construction of fuzzy situational models based on precedents and allows for the ambiguity of the transition with the same control action from one situation to another in cases where it is impossible to ensure the stability of both the uncertainty factors of the external environment and the parameters of the object itself. The method of constructing models based on the consideration of accumulated historical data regarding the operated object is described. This principle largely eliminates the subjectivity inherent in expert methods. In addition, programs implementing the method are distinguished by significantly shorter execution time, which is important for process control systems. A Python program has been developed for conducting computational experiments. The verification, which was carried out using the example of a control system for a conveyor-type roasting machine, confirmed the operability of the proposed method and model.