Mathematical model of adaptive hierarchical high-level control of a three-link collaborative robot-manipulator - Nevliudov, Igor; Yevsieiev, Vladyslav; Maksymova, Svitlana; Artiukh, Roman (INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, 2025)
In the context of the development of the Industry 5.0 concept, which envisages close interaction between humans and intelligent automated systems, research into mechanisms for flexible and safe control of collaborative robot manipulators is becoming particularly important. The objective of this study is to construct a mathematical model of hierarchical high-level control for a three-link collaborative robot that allows the manipulator's behavior to be adapted to dynamic changes in the production environment and the presence of humans in the work area. The purpose of this article is to develop a mathematical model that combines the classical approach to modeling dynamics with the following methods: using Euler-Lagrange equations and modern adaptive control algorithms implemented through fuzzy logic, which allows optimizing the speed and trajectory of the executive bodies under variable load and partial uncertainty of the environment. The proposed model implements a multi-level control architecture that takes into account not only the physical parameters of the manipulator, but also the cognitive aspects of interaction with the operator and the environment by constructing a Fuzzy-Rule-based controller. The article presents the formalization of mathematical expressions, describes all levels of the control system, their purpose and interconnection, and performs numerical modeling using Python and PyCharm. The results of the modeling confirmed the effectiveness of the proposed system in the presence of humans, ensuring a safe distance, adaptation to the load, and compliance with the specified production parameters. Conclusions indicate that the proposed model can significantly improve the quality of manipulator control in Industry 5.0 conditions, especially when working in hybrid environments. The developed model can be adapted to different types of collaborative robots, which opens up prospects for its further improvement, in particular through integration with neural network approaches, deep learning, and multi-agent systems within the framework of distributed intelligent manufacturing.