In rotational motion for various reaches

From the point of view of safety and efficiency of work performed using slewing cranes, the final positioning of the load during the rotational movement of the superstructure is essential. In this paper, the application of an artificial neural network is proposed to solve this problem. The training set was obtained based on dynamic optimization. Exemplary results of numerical simulations for a specific rotation angle and various crane reaches are presented.For many years, research related to the control of the rotational movement of cranes has been conducted in various scientific centers [1], [3-4], [14]. Usually, driving functions are sought that ensure the final positioning of the load for a specific rotation angle [6], [7], [13], although other criteria are obviously also used. In their earlier works, the authors sought the time course of the superstructure rotation driving function, ensuring the final positioning of the load, by means of optimization [7], [10]. This task was carried out for a simplified, numerically efficient crane model. Despite this, the method is time-consuming enough that it cannot be used in real-time. (…)

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