Max Robotics — Global Service Robot Directory + US Certification

About

Jun Oh Jang is a researcher specializing in intelligent control systems, with a focus on neuro-fuzzy networks and their application to robotics and electromechanical systems. His most influential work, "A parallel neuro-controller for DC motors containing nonlinear friction" (2000), has garnered 81 citations and addresses the challenging problem of friction compensation in DC motor systems—a critical issue for precision motion control. Jang's major contributions lie in developing adaptive neuro-fuzzy controllers that integrate linear control with neural network-based compensation for nonlinearities such as friction and deadzone. His research on mobile robot control (2010, 13 citations) and two-robot MIMO systems (2001, 9 citations) demonstrates the practical application of these techniques to complex, multi-agent robotic systems. Notably, his work on "Fuzzy Logic Deadzone Compensation with Feedback Linearization of Nonlinear Systems" (2019) extends his expertise to nonlinear systems with deadzone nonlinearities, showcasing the versatility of fuzzy logic in control. With a career spanning two decades, Jang's research has provided foundational methods for compensating nonlinear effects in motion control, impacting fields from industrial robotics to autonomous vehicles.