RECENT ADVANCES IN ROBOTIC MANIPULATOR MODELING AND INTELLIGENT CONTROL: CHALLENGES AND FUTURE RESEARCH DIRECTIONS

Robotic manipulators have become a cornerstone of modern automation, playing a critical role in industrial production, healthcare systems, space exploration, and intelligent service robotics. These systems are typically composed of multiple interconnected rigid links and actuated joints, forming multi-degree-of-freedom (multi-DOF) structures capable of performing complex spatial tasks with high precision and repeatability. Over the past decades, significant research efforts have been directed toward improving the modeling accuracy and control performance of robotic manipulators, particularly as applications demand higher speed, adaptability, and autonomy in uncertain environments. Traditionally, robotic manipulator modeling has been based on rigid-body dynamics derived from first principles, primarily using the Euler–Lagrange and Newton–Euler formulations. These approaches provide a physically interpretable representation of system dynamics by capturing inertia, Coriolis and centrifugal forces, gravitational effects, and joint interactions. While these classical methods are mathematically rigorous and widely adopted, they are often computationally intensive and highly sensitive to parameter uncertainties such as friction, payload variation, and manufacturing tolerances. As robotic systems become more complex, especially in multi-DOF configurations, these limitations significantly affect real-time implementation and control accuracy.