Papers
16
Total Citations
139
H-Index
6
About
Hong-Zhou Tan is a leading researcher in robotics, control systems, and neural network-based optimization, with a focus on real-time motion planning and redundancy resolution for robotic manipulators. His major contributions include the development of the simplified LVI-based primal-dual neural network for solving linear and quadratic programming problems, a method that has been instrumental in enabling efficient, real-time obstacle avoidance and trajectory control for robots like the PA10 arm. Tan’s work on inequality-based obstacle avoidance and repetitive learning control using quasi-sliding mode has advanced the robustness of nonlinear systems against periodic disturbances. He has also pioneered acceleration-level and jerk-level inverse-free solutions for redundant manipulators, such as the minimum kinetic energy scheme, which eliminates the need for Jacobian inversion and enhances computational efficiency. With over 100 citations across his most influential papers, Tan’s research has had a lasting impact on both theoretical control methods and practical robotic applications. His notable achievements include introducing the E47 and 94LVI algorithms for constrained quadratic programming and applying gradient and Zhang neural dynamics to inverse kinematics, solidifying his reputation as an innovator in intelligent robotic control.
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