Abstract
The paper presents an approach that provides the possibility of identifying nonlinear dependencies of energy consumption of an industrial robot, which is part of an automated technological process, through the use of neuro-fuzzy models. The research group experimentally confirmed the presence of significant differences in the values of the average power consumption of the robot depending on the state of its axes, which, in turn, determines the potential for energy saving when implementing the possibility of choosing the least energy-consuming zones of the robot working area when forming the control program. A complex measurement procedure was performed and a data array of instantaneous power values and corresponding coordinate values was formed. On the basis of a training sample, using the tools of neuro-fuzzy systems, a model of energy consumption of an industrial robot was formed, which allows predicting energy costs when executing a specific control program of the technological process. The parameters of the resulting neuro-fuzzy system and the algorithms implemented in its subsystems are described in detail. Based on the results of representative testing of the model in the entire working area of the industrial robot and correlation with data obtained from measuring devices, the experiment demonstrated the possibility of a significant reduction in energy costs in the range of 7 – 15 %. The ways of integrating the proposed management tools into the production processes of an industrial enterprise are outlined and the tasks of prospective research are defined.
Keywords
Automation, robotics, energy saving, robotic processes, optimization, management.
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