Intelligent control of flexible manipulator link with friction

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Programmable multifunctional rigid manipulators, or industrial robots, require continuous enhancements to meet conflicting demands, such as boosting efficiency and executing complex tasks with high precision across various environments. This necessitates advancements in both the physical robot model and control strategies. The resulting improved systems, known as flexible robot systems (FRSs), utilize lightweight manipulator links with specific geometries characterized by a high aspect ratio. However, these enhancements introduce significant costs, leading to highly nonlinear dynamics and complex coupled mechanical systems due to the flexible nature of lightweight manipulators and the nonlinear friction effects at hub rotor joints. This complexity impacts the positional accuracy of the end effector of the flexible manipulator link (FML). The challenges include developing an accurate mathematical model of the FRSs that accounts for friction and designing effective active control strategies to achieve desired tasks with precision across various operating configurations. This work aims to systematically identify and optimize a mathematical dynamic model of the FRS with friction. Once a reliable model is established, the study will investigate and refine proposed direct and learning control strategies based on a knowledge-based fuzzy logic system, utilizing simulation techniques before real-world implementation for validation.