Papers
13
Total Citations
217
H-Index
7
About
Xiaozheng Jin is a control systems researcher whose work sits at the intersection of robust adaptive control, fault-tolerant systems, and intelligent robotics. His research primarily addresses the challenge of maintaining reliable performance in robotic systems when components fail or parameters are unknown — a critical concern for real-world deployment of autonomous machines. Jin's most influential contribution, his 2019 study on fault-tolerant control of wheeled mobile robots (53 citations), tackled actuator faults without requiring prior knowledge of fault information, a significant practical advancement. This theme of designing controllers resilient to uncertainty extends across his portfolio, including multi-agent consensus control with circuit implementation (39 citations) and trajectory tracking under actuator faults. His work on robotic fish steering using a novel discrete-time super-twisting algorithm (42 citations) demonstrates versatility beyond terrestrial platforms. Jin has progressively integrated machine learning techniques into classical control frameworks, employing extreme learning machines, neural networks, and reinforcement learning to handle nonlinear dynamics in manipulators and mobile robots. His exploration of agricultural robotics and distributed cooperative control of multiple manipulators reflects a broadening scope toward real-world applications. With over 200 cumulative citations and publications spanning leading control journals, Jin's contributions meaningfully advance the robustness and intelligence of modern robotic control systems.
Research Focus
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