АЛГОРИТМЫ КИНЕМАТИЧЕСКОГО УПРАВЛЕНИЯ МНОГОЗВЕННЫМИ МАНИПУЛЯЦИОННЫМИ РОБОТАМИ НА ОСНОВЕ НЕЧЁТКОЙ НЕЙРОННОЙ СЕТИ

Abstract

The paper considers the possibility of constructing a kinematic control algorithm for manipulating robots with a serial-connected links. The construction of a control system based on a fuzzy neural network is proposed. The results of experimental studies on the selection of parameters of a fuzzy neural network in accordance with the set optimality criterion (in terms of speed), taking into account the subsequent iterative refinement by the Newton-Raphson method, are presented. The following network parameters are considered: the number and type of node membership functions, the size of the training sample with a different number of training approaches. An algorithm for forming a training sample for fuzzy neural networks is proposed in order to reduce the positioning error of the working body of the manipulating mechanism near the outer boundary of the workspace. The possibility of adapting the kinematic control algorithms by adjusting the parameters of the membership functions in the network nodes when performing the same type of tasks, based on the data of the Newton-Raphson refinement algorithm, is demonstrated. In the framework of this work, a comparative analysis of the developed kinematic control algorithm with algorithms based on iterative and neural network methods for solving the inverse kinematics problem of a manipulative robot is carried out. The conclusion is made about the increase in the speed for calculations of kinematic control algorithms while maintaining the required accuracy