Sanam Hajipour

Brandenburg University of Technology Cottbus-Senftenberg

Papers

Total Citations

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About

Sanam Hajipour is a researcher at the forefront of merging machine learning with advanced control systems, with a primary focus on the tracking control of complex multibody and closed-loop mechanisms. Her most cited work, "Design of a Tracking Controller Based on Machine Learning" (2025, 15 citations), tackles a notoriously difficult problem in robotics and mechanical engineering: the computational bottleneck of inverse kinematics in closed-loop systems. By integrating machine learning into the controller design, Hajipour offers a transformative approach that bypasses the need for exhaustive, real-time calculations, making precise tracking more efficient and accessible. This contribution is particularly impactful for applications in autonomous systems, robotics, and industrial automation, where rapid and accurate motion control is critical. While still early in her career, her work signals a significant shift toward data-driven solutions in classical control challenges, positioning her as an emerging innovator in the field. Researchers and students interested in the intersection of artificial intelligence and mechanical system dynamics will find her approach both novel and practically promising.