S. Mahmoudkhani

Papers

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About

S. Mahmoudkhani is a researcher specializing in nonlinear system identification and friction modeling, with a focus on developing iterative algorithms for parameter estimation. Their most cited work, "A new iterative identification algorithm for estimating the LuGre friction model parameters" (2024), has garnered 17 citations, reflecting its impact on advancing precision control in mechanical systems. This contribution addresses a critical challenge in robotics and automation by improving the accuracy of friction compensation, which is essential for high-performance motion control. Mahmoudkhani’s research bridges theoretical algorithm design and practical engineering applications, offering robust solutions for systems where friction dynamics significantly affect performance. Their work is notable for introducing a novel iterative approach that enhances convergence and reduces computational complexity compared to traditional methods. By tackling the LuGre model—a widely used but computationally demanding friction representation—Mahmoudkhani has provided a valuable tool for engineers and researchers in mechatronics and control systems. Their contributions are particularly relevant for students and practitioners seeking efficient, real-time identification techniques, positioning them as a promising voice in the field of nonlinear system identification.