Neural Network Tuning of the Genetic Algorithm for Controlling a Vector Hierarchical System

The article presents the results of a study of a hierarchical two-level vector control system for a multi-connected object whose evolution is described by a state vector that changes in response to actions on actuators, each of which includes a drive and a working mechanism. The control system under consideration is distinguished by the presence of an additional tuning loop for the virtual regulator at the upper and functional-logical levels. A mathematical model of the impulse response of the actuator of the system is synthesized, taking into account dry friction, backlash, and limitations on the speed and position of the working element of the controlled object. The original model of the actuator is presented in the Cauchy form, and its impulse response is approximated by the impulse response of a second-order linear link, optimal according to the criterion of the minimum approximation error. It is proved that the parameters of the linearized impulse response depend on the operating parameters of the drive. A model of a closed control system for the object as a whole is constructed, and it is shown that its parameters depend on the operating parameters of the drives, the desired value of the state vector of the control object, and the parameters of the virtual controllers implemented at the functional-logical and upper hierarchical levels. The obtained results demonstrate that a change in the operating parameters of the object can be compensated for by structural and parametric changes in the genetic control algorithm. A technique for synthesizing a genetic control algorithm for complex multi-loop objects implemented by a controller at the upper level of the hierarchy based on the use of a neural network has been developed. It is shown that the proposed approach ensures the achievement of a synergetic effect, when the control actions implemented by different modifications of the control algorithm are less effective than the control actions implemented using a composite algorithm subject to evolutionary changes during the operation of the system. The correctness of the theoretical positions is confirmed by the results of computational modeling of the virtual controller control using a neural network, which demonstrated a significant improvement in the control characteristics due to a decrease in the time to reach a steady state and the overshoot time.