Papers
16
Total Citations
1,224
H-Index
9
About
Mou Chen is a prominent control systems researcher whose work centers on robust and intelligent control of robotic systems, with particular emphasis on underwater robots, mobile robots, and manipulators. He is best known for developing advanced disturbance-rejection and observer-based control frameworks that address real-world challenges such as unknown disturbances, uncertain nonlinearities, and complex dynamics in robotic platforms. His most influential contribution, "Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities" (2017), has accumulated over 750 citations across its original and corrected publications, establishing him as a leading voice in underwater robotics control. By combining MIMO extended state observers with integral sliding mode control, Chen's approach elegantly handles unmeasured velocities and uncertain hydrodynamics — longstanding challenges in the field. His complementary work on wheeled mobile robots and self-balancing platforms, employing disturbance observer-based tracking control, further demonstrates the breadth of his expertise across robotic domains. Chen has also made contributions in neural network-based adaptive control, Q-learning-driven impedance control, and fractional-order control of chaotic systems, reflecting a genuinely interdisciplinary research vision. His body of work provides both theoretical rigor and practical solutions that continue to guide researchers tackling robustness challenges in autonomous robotic systems.
Research Focus
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Top Papers
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- 9Fractional-order control for a novel chaotic system without equilibrium12 citations · 2016
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