Cungen Liu
Papers
4
Total Citations
29
H-Index
3
About
Cungen Liu is a rising researcher in adaptive and optimal control theory, with a focus on nonlinear and stochastic systems. Their work addresses critical challenges in system stability, robustness, and performance under uncertainty. Liu’s most cited paper (2024, 14 citations) introduces a finite-time adaptive optimal control strategy for uncertain strict-feedback nonlinear systems, integrating fuzzy observers and reinforcement learning to achieve global optimization—a novel fusion of learning-based and classical control methods. Another key contribution (2023, 9 citations) develops an adaptive controller for stochastic systems with uncertain virtual control gains and input nonlinearities, employing a novel auxiliary function to ensure boundedness and smoothness. Liu also advances robotic control with a mismatched composite disturbance observer-based adaptive tracking controller for motor-driven manipulators (2024, 5 citations), relaxing restrictive assumptions on disturbance derivatives. Their latest work (2024) proposes a dynamic finite-time prescribed performance control strategy with enhanced constraint handling and robustness. With a growing citation record, Liu’s research bridges theoretical rigor and practical applicability, offering innovative solutions for complex, real-world control systems.
Research Focus
Key Achievements
Top Papers
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