Ali Bazaei

Western University, Ilam University, Tarbiat Modares University

Papers

Total Citations

H-Index

About

Ali Bazaei is a control systems researcher whose work focuses on intelligent and adaptive control strategies for complex nonlinear systems, particularly in robotics and multi-input multi-output (MIMO) dynamics. His research integrates neural networks, approximate feedback linearization, and friction compensation to address real-world challenges in system identification and motion control. Bazaei’s most cited paper (11 citations) introduces an online neural identification method for MIMO systems, leveraging Lyapunov stability theory to ensure robust approximation of nonlinear functions—a foundational contribution to neuro-adaptive control. In another notable work (4 citations), he develops an approximate feedback linearization scheme for robot manipulators, reducing control law complexity by exploiting the concept of zero Riemannian curvature in an idealized inertia matrix. His research on flexible-link robots (2 citations) extends black-box neural control to a serial-gray-box modeling strategy, specifically targeting unknown friction torques while preserving prior system knowledge. Though his citation counts are modest, Bazaei’s work demonstrates technical rigor in bridging theoretical control design with practical implementation challenges, offering valuable insights for students and researchers in adaptive control, robotics, and nonlinear system identification.