Papers
18
Total Citations
175
H-Index
7
About
Xinye Zhu is a researcher specializing in advanced control theory for robotic systems, with a particular focus on modular robot manipulators, game-theoretic optimal control, and human-robot interaction. Their work sits at the intersection of reinforcement learning, adaptive dynamic programming (ADP), and multi-agent game theory, addressing the persistent challenge of controlling complex robotic systems under uncertain disturbances. Zhu's most influential contribution, a zero-sum game-based neuro-optimal control framework using critic-only policy iteration (50 citations), demonstrated how adversarial game formulations could yield robust, computationally efficient controllers for modular manipulators. Building on this foundation, their research expanded into nonzero-sum and mixed-zero-sum games, fuzzy logic approximation, and Stackelberg differential game strategies, collectively garnering over 150 citations across a rapidly growing body of work. Notably, their integration of sliding mode control with ADP methods for position-force control of reconfigurable manipulators highlighted practical applicability to constrained robotic tasks. More recently, Zhu has advanced the field of human-robot collaboration, developing decentralized cooperative game strategies, event-triggered control architectures, and Gaussian process-based motion intention estimation — work that bridges theoretical rigor with real-world human-centered robotics. Their trajectory reflects an evolving research vision: making intelligent robotic systems safer, more adaptive, and genuinely collaborative partners in human environments.
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