Authors Y.I. Eremenko, D.A. Poleshchenko, A.I. Glushchenko
Month, Year 10, 2015 @en
Index UDC 004.89 + 681.51
Abstract A neural tuner is used to increase energy efficiency of unsymmetrical plants described by first or second order aperiodic links with time delay. It allows to tune KP and KI parameters of a PI-controller online without knowledge of plants model. A main part of the tuner is a neural network, which input vector includes plant output value signals delayed on equal time gaps Δt from each other. The main aim of the research is to develop a method to calculate Δt depended on plant parameters values. More than 15000 experiments are conducted with plant models using different values of time constant, plant gain and delay time. Δt values are changed from 1 s to 40 s for each certain model. The best experiment is chosen from the set of 40 on the basis of proposed criteria. Such experiment shows the best value of Δt for the plant model in question. Having conducted experiments, sought analytical dependence is found. It is also shown that the number of the neural tuner calls N during each transient of each experiment with the best value of Δt is a constant. The neural tuner is called every Δt seconds. It is also shown that N value probability curve is Gaussian distribution. Such facts are proved theoretically. On that base a method to calculate Δt value without knowledge about plant parameters values is proposed. Further research needs to be done to include parameter Δt into neural network operative training process to be able to refine it during control system functioning. In that case obtained analytical dependence will be used to initialize Δt.

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Keywords Adaptive control; neural networks; PI-controller; neural tuner; input signals time delay.
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