Jiliang Zhang
Papers
3
Total Citations
183
H-Index
3
About
Jiliang Zhang is a leading researcher in the fields of neural dynamics, robotics, and human–machine interaction, with a focus on solving complex optimization and control problems. His major contributions include the development of complex-valued discrete-time neural dynamics for perturbed time-dependent complex quadratic programming, a breakthrough that extends traditional real-domain recurrent neural networks to handle complex-valued variables—critical for applications in signal processing and robotics. This work has garnered 99 citations, reflecting its foundational impact. Zhang also introduced a joint-drift-free scheme using projected zeroing neural networks for redundant robot manipulators under disturbances, addressing a critical failure mode in robotic motion generation (66 citations). More recently, his 2024 work on recurrent neural network-enabled continuous motion estimation from incomplete sEMG signals (18 citations) advances exoskeleton control by decoding human motion despite sensor failures, a practical step toward robust assistive technologies. Zhang’s research is notable for bridging theoretical neural dynamics with real-world robotic and biomedical applications, earning recognition for its innovation and utility in enhancing system reliability and performance.
Research Focus
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Top Papers
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