Intellectualisation of the decision support system by a multiagent robotic system based on neural network technology

Abstract

The article considers the process of forming management decisions for the correct operation of a multi-agent robotic system (grouping of unmanned aerial vehicles (UAVs)) in a rapidly changing conditions. A neural network (NN) is used as a regulator, which is a perceptron with two intermediate layers, one is input and the other one is output. The training of the constructed neural network is carried out according to an algorithm that combines the ideas of the conjugate gradient method with quasi-Newtonian methods, and in particular, uses the approach implemented in the Levenberg-Marquardt algorithm. As input parameters for building a control system, various types of situations are used in which the UAV leader has to make decisions. Such initial data are a three-dimensional data structure, which is a two-dimensional array, each element of which is a vector. One vector reflects all available information about the situation in certain geographic coordinates (weather conditions, data on other damaging factors affecting the ability of the UAV to operate in a certain area), and the other describes the current state of the UAV (power reserve, weight, operability of components and assemblies, term of next service, etc.). The obtained information is put down into the input layer of the NN by numerical equivalents. The paper proposes a situational work model of a UAV group in special conditions.