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Model of control of a multi-link robot manipulator under conditions of environmental uncertainty

The structure of the control model of a multi-link robot manipulator is proposed, the distinctive feature of which is the inclusion of blocks for solving problems of direct and inverse dynamics using the fuzzy interval method. The relevance of the research topic is characterized by the need to develop and implement robotic systems to replace human labor in hazardous and harmful industries, as well as to improve the algorithmic support of robot control systems in conditions of environmental uncertainty. Algorithms for solving direct and inverse problems of the dynamics of multi-link robot manipulators have been developed, based on a description of the motion of MLRM links in the form of a system of equations that take into account the uncertainties of the external environment, modeled by fuzzy logic methods. The object of the study was the process zones in the immediate vicinity of small-ore pelletizing units and ore heating furnaces of mining and processing plants, where there are uncertainty factors of the external environment of two main groups: the first includes factors that complicate the determination of the coordinates of the target object of the MLRM capture (for example, as a result of the dustiness of the environment), the second – factors affecting the movement of the moving parts of the MLRM (for example, caused by wear or heating of parts of the mechanisms). Testing of the proposed algorithms was carried out in a model experiment in the MatLab environment using the Simscape physical modeling tools, as well as the Robotics System Toolbox for designing, modeling and testing robotic applications. The experiment showed that the accuracy of positioning the end effector of the MRM in the case of using the proposed interval method, although it is not a fraction, but several percent of the specified terminal position, but exceeds the solutions obtained using the standard Robotics System Toolbox tools, which are not adapted to work in conditions of environmental uncertainty. Read more...