Ling-Wei Kong

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About

Ling-Wei Kong is an emerging researcher working at the intersection of machine learning, nonlinear dynamics, and control theory, with a particular focus on developing intelligent, data-driven approaches to complex system control. His most notable contribution, "Model-free tracking control of complex dynamical trajectories with machine learning" (2023), has rapidly garnered 67 citations, signaling its significant impact on the robotics and control engineering communities. This work addresses a longstanding challenge in control theory: traditionally, designing effective tracking control requires complete knowledge of a system's underlying equations and model. Kong's approach circumvents this requirement entirely, enabling dynamical systems to follow desired trajectories using machine learning alone — a breakthrough with broad implications for both civil and defense robotics applications. Beyond model-free control, Kong has also explored the nuanced problem of complex object manipulation, investigating how humans intuitively manage systems with internal degrees of freedom — such as carrying a cup of liquid while walking — in his work on synchronous transitions in complex object control (2021). Together, these contributions reflect a research vision aimed at bridging human intuition, physical dynamics, and computational intelligence, positioning Kong as a promising voice in the next generation of control and dynamical systems research.