Papers
5
Total Citations
149
H-Index
3
About
Peng Miao is a leading researcher in neural dynamics and robotic control, with a primary focus on developing advanced neural network models for real-time robotic applications. His most influential work, "Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking" (2014), has garnered 128 citations, establishing a foundational framework for using finite-time Zhang neural networks (ZNN) to solve complex time-varying optimization problems in robot tracking. Miao’s contributions extend to discrete-time neural networks with bias noises for time-variant matrix inversion (2019), and he has pioneered fixed-time zeroing neural network models (2024) that ensure rapid, robust convergence for path tracking control of wheeled mobile robots and photovoltaic cleaning robots. Notably, his work on trajectory tracking control of photovoltaic cleaning robots (2018) integrates Lyapunov theory and Barbalat’s lemma to create universal crawler robot motion models, addressing practical challenges in renewable energy maintenance. With a career spanning over a decade, Miao’s research bridges theoretical neural network design and tangible robotic systems, impacting fields from industrial automation to clean energy. His recent fixed-time ZNN models (2024) represent a significant leap, offering guaranteed convergence times independent of initial conditions, making his work essential for students and researchers in control theory, robotics, and applied mathematics.
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