Chengzhe Han

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About

Chengzhe Han is a rising researcher at the intersection of nonlinear control theory and aerial robotics. His work primarily focuses on robust model predictive control (MPC) for complex, safety-critical systems and motion planning for hybrid aerial-terrestrial manipulators. In his most cited work, Han tackled the challenging problem of bumpless transfer MPC for hidden Markovian jump nonlinear systems with saturated inputs, providing a rigorous framework for T–S fuzzy systems that ensures stability even under bounded disturbances—a scenario often overlooked in existing literature. This paper has already garnered 14 citations since its 2024 publication, signaling strong impact in the control community. Han is also known for his work on Chat-PM, a novel hybrid vehicle equipped with a robotic arm. He developed a rotorwash-aware motion planning method that enables the system to grasp lightweight objects despite severe aerodynamic interference from spinning rotor blades, a practical breakthrough for aerial manipulation. His subsequent robust control design for Chat-PM, modeled as a switched singular system with mode-dependent bounded nonlinearity, further demonstrates his ability to bridge theory and real-world application. With a growing citation record and contributions spanning both theoretical control and applied robotics, Han is establishing himself as a versatile engineer advancing autonomous systems in uncertain environments.