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About

Dr. Zeqi Yang is a leading researcher in the field of robotic control systems, with a primary focus on adaptive impedance control, neural network-based force tracking, and uncertainty compensation in robotic manipulators. His most significant contributions lie in developing advanced control frameworks that enable robots to interact safely and precisely with uncertain environments. Dr. Yang’s seminal 2018 work on adaptive neural network force tracking impedance control, which has garnered 99 citations, introduced a novel nonlinear velocity observer to address force tracking challenges in uncertain robotic systems. This was further extended in his 2019 paper on position/force tracking impedance control using adaptive Jacobian and neural networks (42 citations), where he proposed a method to achieve precise force control indirectly through position tracking, even under external disturbances. His 2020 study on neuro-adaptive observers for electrically driven robotic systems (40 citations) and his 2019 work on hybrid position/force control without velocity measurement (21 citations) have collectively shaped modern approaches to robust, sensor-reduced robotic manipulation. With over 200 total citations, Dr. Yang’s research is instrumental for students and engineers working on human-robot interaction, rehabilitation robotics, and industrial automation, offering practical solutions for safe and adaptive robot behavior in real-world applications.