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About

Maryam Dehghani is a robotics and control systems researcher whose work focuses on the adaptive control of rigid-link electrically driven robots, with particular emphasis on addressing real-world uncertainties that challenge reliable robotic operation. Her research tackles the fundamental problem of designing robust tracking controllers when kinematics, manipulator dynamics, and actuator dynamics are incompletely known — a critical concern in practical robotic deployment. Dehghani's most significant contributions center on the application of backstepping strategies to develop adaptive controllers that gracefully handle parametric uncertainties across multiple system layers simultaneously. A notable thread running through her work is the elimination of acceleration measurements, a practically valuable innovation since accelerometers introduce noise and cost in robotic systems. Her 2011 paper on adaptive backstepping control represents her most-cited contribution with 5 citations, followed by complementary work extending these methods to task-space control frameworks. Collectively, her publications have garnered 12 citations, reflecting a focused body of work within the specialized field of electrically driven robot control. Her research provides theoretical foundations and practical tools for engineers designing more reliable, uncertainty-tolerant robotic systems, making her contributions relevant to both academic researchers and practitioners working on advanced robot motion control.