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About

Yin Jiang-ping is a robotics and computational intelligence researcher whose work centers on the kinematic and dynamic control of redundant robot manipulators, with a particular focus on resolving the challenge of manipulator redundancy through advanced optimization techniques. His most significant contributions lie in the development of bi-criteria weighting schemes that combine two-norm and infinity-norm optimization criteria to address critical instability issues — notably discontinuity points and torque instability — that plague conventional single-norm approaches in joint-acceleration and joint-velocity control. By formulating these problems within a primal-dual neural network framework, specifically leveraging the LVI-based (Linear Variational Inequality) primal-dual architecture, Yin has advanced real-time, computationally efficient solutions for redundant manipulator control that respect practical joint physical constraints such as velocity and acceleration limits. His 2011 paper on two/infinity norm criteria at the joint-acceleration level stands as his most cited contribution with 8 citations, while his foundational 2006 and 2007 studies collectively demonstrate a sustained research trajectory refining bi-criteria acceleration minimization. Though operating within a specialized niche, Yin's methodological innovations offer meaningful practical value to researchers and engineers designing robust, smooth-motion robotic systems.