Jidong Liu
Papers
3
Total Citations
36
H-Index
3
About
Jidong Liu is a leading researcher in nonlinear control systems and intelligent robotic fabrication, whose work bridges theoretical rigor with practical automation challenges. His most influential contribution, the 2020 paper "Almost fast finite‐time adaptive tracking control for a class of full‐state constrained pure‐feedback nonlinear systems" (26 citations), introduces a novel barrier Lyapunov function-based scheme that achieves rapid, stable tracking under strict state constraints—a critical advance for safety-critical applications like exoskeletons and autonomous vehicles. In robotic manufacturing, Liu tackles real-world complexity through his 2024 work on "Proxy-Based Sliding Mode Force Control for Compliant Grinding," which integrates diagonal recurrent neural networks with Prandtl-Ishlinskii hysteresis compensation to enable high-precision force control for surface finishing, addressing the rigidity limitations of industrial robots. Most recently, his 2025 study "A neural network-based model for assessing 3D printable concrete performance in robotic fabrication" (5 citations) pioneers a backpropagation neural network that predicts printability based on flowability and strength, directly optimizing mix designs for construction-scale additive manufacturing. With a growing citation impact across control theory and digital fabrication, Liu exemplifies how adaptive control and machine learning can transform both theoretical nonlinear dynamics and real-world robotic processes.
Research Focus
Key Achievements
Top Papers
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