Amin Yousefpour

University of Tehran, University of California, Irvine

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About

Amin Yousefpour is a control systems researcher whose work bridges advanced nonlinear control theory with real-world mechanical and nano-scale applications. His primary research areas include robust and adaptive control, sliding mode control, neural network-based control, and fault-tolerant systems. Yousefpour’s most impactful contribution is a 2020 study on a non-holonomic spherical robot, where he developed a recurrent neural network-based robust nonsingular sliding mode control scheme that handles input saturation—a critical challenge in mobile robotics. This paper has earned 52 citations, reflecting its influence in the field of autonomous and underactuated systems. More recently, he has extended his expertise to nano-mechanics, proposing a fault-tolerant terminal sliding mode controller with a disturbance observer for vibration suppression in non-local strain gradient nano-beams. This work, published in 2023, addresses stabilization of uncertain Euler–Bernoulli nano-beams, showcasing his ability to tackle complex, multi-physics problems at the micro and nano scales. Yousefpour’s research is notable for its rigorous mathematical foundation and practical relevance, making him a rising figure in both classical control engineering and emerging nano-technology control applications.