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About

Amir Ghiasi is a robotics and control systems researcher whose work bridges theoretical rigor with practical robotic locomotion. His primary research areas include bipedal robot gait design, nonlinear control, and multi-agent systems. Ghiasi’s most influential contribution is his 2009 paper on the simultaneous design of optimal gait patterns and controllers for bipedal robots, which has garnered 22 citations and laid foundational work for integrated motion planning. He further advanced robotic manipulation with his 2015 study on designing an adaptive robust extended Kalman filter based on a Lyapunov-based controller, achieving 14 citations by addressing position and velocity estimation under bounded disturbances. His 2011 work on dynamic modeling and sliding mode control for fast walking of seven-link biped robots simplified equation derivation while accounting for ground impact, contributing to efficient locomotion. More recently, Ghiasi explored scaled consensus in descriptor multi-agent systems (2017), extending control theory to complex system dynamics. Through these contributions, he has demonstrated a consistent focus on enhancing robotic autonomy and stability, making his research valuable for students and engineers working on legged robots, manipulator control, and distributed systems.