Neural network model of robotic complex control during emergency rescue operations under the conditions of the Far North

The geopolitical situation and the increase in criminal and terrorist threats dictate the need to develop new territories, including the North of Russia. The article presents the results of a study of an artificial neural network mathematical model that takes into account delays designed to control an autopiloted ground-based robotic complex employed for transportation during emergency rescue operations under difficult natural and climatic conditions of the Arctic region. The universal nature of the proposed method is shown. An approach to finding a solution to the optimization problem using the necessary optimality conditions in the form of the Pontryagin maximum principle and the method of rapid automatic differentiation is described. A problem-oriented software product has been created, which is based on the developed computational algorithm for constructing approximate optimal control. The results obtained from numerical experiments confirm the effectiveness of the developed algorithm in finding an approximate optimal solution to the problem under consideration. The created software tool can be used to train an ANN with dynamics described by a system of differential equations with consideration to delays. The proposed mathematical model of ANN is suitable to solving a wide range of applied robotics tasks, including those aimed at developing technical means for emergency rescue operations under difficult natural and climatic conditions of the Arctic region. The flexibility, stability and adaptability of the selected model to changes in input parameters determine the prospects of using the developed computational algorithm to solve control problems in complex technical systems.

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