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About

Arash Arjmandi is a robotics researcher whose work focuses on the control and motion planning of robotic manipulators, particularly for surgical and industrial applications. His primary research areas include nonlinear control theory, adaptive impedance control, and disturbance rejection in robotic systems. Arjmandi's major contributions center on developing robust control strategies that enable precise tracking and stability in the presence of bounded disturbances and parameter uncertainties. His most cited work, "Designing inverse dynamic controller with integral action for motion planning of surgical robot in the presence of bounded disturbances" (2013, 9 citations), addresses critical challenges in robotic laparoscopic graspers, such as achieving high accuracy and disturbance rejection for minimally invasive surgery. He further advanced the field with "An observer-based adaptive impedance-control for robotic arms: Case study in SMOS robot" (2016, 5 citations), which introduces an adaptive output-feedback control method for environment-machine interaction. His comparative study "Evaluation of Three Nonlinear Control Methods to Reject the Constant Bounded Disturbance for Robotic Manipulators" (2020, 2 citations) systematically assesses integral action and tracking controllers to improve tracking accuracy. Arjmandi's work is valuable for researchers and engineers developing reliable, high-precision robotic systems for medical and industrial settings.