Structural-parametric adaptation of fuzzy-logical control system

The article considers the issues of creation of high-efficiency algorithms for control of technological facilities, functioning in conditions of uncertainty. The algorithm of structural-parametric adaptation of fuzzy-logical PID-regulator is proposed, which allows ensuring high speed of the control system due to reduction of the number of iterations during training, that is, the number of neural network training epochs. A fast fuzzy-logic output algorithm has been developed to eliminate empty solutions and zero sections in terms that describe input and output fuzzy variables. A structural diagram of automated control systems of technological processes has been developed, which includes an adaptation unit, which allows correcting not only the parameters, but also the structures of the control stages. Proposed structural-parametric adaptation in control tasks of technological process equipment allows accelerating process of system training, due to use of high-speed algorithm of fuzzy-logical output, to reduce error of results of training of neuro-fuzzy network from 8 to 1%. An algorithm for training the neural network was developed, based on the use of the soft computation method, using the area difference of the belonging function. Based on the simulation experiment, it is determined that the accuracy of training in the existing methods is about 5%, therefore one parameter must be corrected, by change in the third or fourth layer of the neuro-fuzzy network fuzzy-logical operations, that is, instead of min, use the max operation, which allows to obtain a given result in fewer iterations than in the known methods.