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About

Mohammad Azimi is a control systems and robotics researcher whose work focuses on the intelligent control of complex, underactuated robotic systems. His most influential contribution, "Adaptive fuzzy backstepping controller design for uncertain underactuated robotic systems" (2014), has garnered 46 citations, establishing a robust framework for handling nonlinear dynamics and system uncertainties. Azimi has also advanced the field of mobile robotics through his work on model predictive control for two-wheeled self-balancing robots (2013, 23 citations), addressing the challenges of multivariable underactuated dynamics. More recently, he has explored the intersection of control theory and fault diagnosis, investigating simultaneous fault detection and control for fractional-order robotic models. In a forward-looking application, Azimi integrated neural-network-based computer vision with a 2-DOF robot for real-time object tracking (2023). His research trajectory demonstrates a consistent commitment to bridging theoretical control methods—such as fuzzy logic, predictive control, and fractional-order systems—with practical robotic implementations, making his work relevant for students and researchers interested in autonomous systems and intelligent control design.