Shijia Kang

Bohai University, Beijing Jiaotong University

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About

Shijia Kang is a leading researcher in advanced nonlinear control systems, with a primary focus on adaptive neural and fuzzy control for flexible robotic manipulators. Dr. Kang’s work addresses critical challenges in robotic actuation, particularly the compensation of input dead-zone nonlinearities and multiple actuator constraints. In their highly cited 2019 paper, Kang developed an adaptive neural network command filtered tracking control method that treats dead-zone input as a combination of linear and bounded disturbance-like terms, enabling precise trajectory tracking for flexible robotic arms. This foundational work has garnered 36 citations, establishing Kang as a key contributor to robust robotic control. More recently, in 2023, Kang advanced the field by proposing a fuzzy finite-time position tracking control strategy for single-link flexible-joint robots facing multiple actuator constraints, using fuzzy logic systems to estimate unknown nonlinear functions. With 14 citations in a short period, this work demonstrates Kang’s continued impact on achieving rapid, stable control under real-world limitations. Dr. Kang’s research is essential reading for engineers developing high-performance, fault-tolerant robotic systems.