Xiaonan Zhao

Changchun University of Science and Technology

Papers

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About

Xiaonan Zhao is an emerging leader in robotics and computational intelligence, whose work bridges bio-inspired optimization and advanced control theory. Their primary research areas include meta-heuristic algorithm design, robotic manipulator control, and nonlinear system dynamics. Zhao’s most notable contribution is the Wild Geese Migration Optimization (GMO) algorithm, a novel meta-heuristic inspired by the social swarming behavior of wild geese. This algorithm, detailed in their 2022 paper with 37 citations, offers an innovative solution to the complex inverse kinematics problem in robotics, demonstrating how natural collective strategies can enhance computational efficiency. Building on this foundation, Zhao’s 2023 work introduces a parallel network-based sliding mode tracking control for robotic manipulators, addressing critical challenges of uncertain dynamics and external perturbations. By proposing a parallel network (PCR) to compensate for dynamic model inaccuracies, this research significantly improves control accuracy and stability in real-world robotic applications. Zhao’s work is distinguished by its practical impact, offering robust solutions for autonomous systems operating under unpredictable conditions. Their contributions are particularly valuable for students and researchers in robotics, control engineering, and swarm intelligence, providing both theoretical insights and implementable frameworks for next-generation robotic systems.