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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...

Fuzzy bioinspired method for forming a set of candidates for linear positions

Line personnel occupy the vast majority of positions in many organizations, which determines the importance of timely and successful filling of such vacancies. The search for candidates for such positions is carried out through mass recruitment, which is characterized by high labor intensity, budgetary and time constraints, and the need for regular repetition due to high staff turnover rates. The noted features make it impossible to carry out this process without the use of modern software. Since mass recruitment does not require finding the best candidate for each vacancy and is limited to searching for specialists based on formal criteria from their resume, the main share of labor and time costs falls on the primary selection of candidates. Existing software does not have sufficient functionality to effectively automate this process. Given the need to process large volumes of multidimensional data, they do not provide a comprehensive accounting of different types of candidate characteristics and automatic adjustment of selection criteria taking into account their priority for the vacancy being filled. To solve the problem, an automated method for forming a set of candidates for linear positions was developed. It is based on the integrated use of an adaptive neuro-fuzzy inference system and a bioinspired algorithm inspired by the behavior of a fish school. The developed hybrid method was implemented as a computer program using the Python language. The results of its testing showed the convergence of the optimization algorithm, and their comparison with manual selection confirmed the prospects for using it to solve tasks of mass recruitment of line personnel. Read more...

Generalized approach to building fuzzy bioinspired models for situational project management

When managing complex projects related to the development and organization of production of innovative products, the decision-making process is influenced by many situational aspects. This complicates the assessment of the quality of decisions, which are usually multi-variant and require taking into account random influences. In such cases, a significant effect can be achieved by using bioinspired methods that allow one to find a solution acceptable for a specific situation, in which elements of fuzzy set theory are used to describe ­NON-factors. The article proposes a generalized approach to creating a model based on the specified methods, which is intended to support decision-making in managing an innovative project. This model is distinguished by the comprehensive use of fuzzy bioinspired methods for selecting and justifying options for action in strategic and operational planning and situational management of project activities, taking into account the general and specific characteristics of the project stages, as well as the dynamic nature of external and internal factors. The proposed approach forms the basis of the developed fuzzy method for selecting equipment for conducting experimental design work and organizing the production of innovative products using a model of the behavior of a pack of wolves during hunting. The method is distinguished by the use of a fuzzy Euclidean measure of proximity between the quality indicators of the options being evaluated and the three best ones selected at a given iteration (alpha, beta, and delta solutions) to determine the direction of the search for a rational set of equipment, a modification of the rules for searching for solutions (movement of individuals) based on the consideration of the “depth of matches” and the increment of the effect, including for finding a reasonable balance between directed and random search, and the use of a base of fuzzy production rules when choosing a method for forming the basis for an alpha solution at subsequent iterations. The method is implemented in Python 3.12.0. The effectiveness of the proposed approach is confirmed by data from a computational experiment. Read more...