Papers
9
Total Citations
332
H-Index
7
About
Mingzhi Mao is a computational intelligence researcher whose work sits at the intersection of neural network theory, numerical optimization, and robotic control systems. His research has made substantial contributions to the development of **zeroing neural network (ZNN)** methodologies, particularly in solving time-varying matrix inversion, nonlinear optimization, and different-level dynamic linear systems — problems of considerable complexity that arise frequently in real-world engineering contexts. Mao's most influential contributions include enhanced discrete-time ZNN formulations robust to bias noise (66 citations), Z-type neural dynamics for constrained nonlinear optimization (64 citations), and a unified framework for continuous and discrete ZNN applied to robot manipulator control (57 citations). His 2018 work establishing general square-pattern discretization formulas represented a principled theoretical advance over prior ad hoc approaches (52 citations). More recently, he has extended his expertise toward cerebellum-inspired visual servo control of dual robotic arms with unknown kinematics (39 citations) and fuzzy-enhanced robust discrete ZNN models for multi-constrained optimization (28 citations). Across his career, Mao has accumulated over 330 citations, reflecting his growing influence in neurodynamics-driven robotics. His adaptive zeroing neurodynamics models signal an important evolution toward more intelligent, self-correcting neural systems — a promising direction for next-generation autonomous robot control.
Research Focus
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Top Papers
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- 7Different-level algorithms for control of robotic systems14 citations · 2019
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