Papers
44
Total Citations
513
H-Index
11
About
Tianjiao An is a prominent researcher specializing in optimal control, game theory, and robotics, with a particular focus on modular robot manipulators (MRMs) and human-robot collaboration (HRC). His work sits at the intersection of adaptive dynamic programming, neuro-optimal control, and multi-agent game-theoretic frameworks, addressing some of the most pressing challenges in intelligent robotic systems. An's most celebrated contribution, "Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human–Robot Collaboration" (2023), has garnered an impressive 104 citations, demonstrating its significant influence on the field. This work tackles the dual challenge of estimating human motion intention and optimizing performance in collaborative robotic settings. Across his portfolio, he has pioneered both zero-sum and nonzero-sum game-based control strategies, developing robust decentralized architectures—including actor-critic-identifier structures and critic-only policy iteration—that enable MRMs to operate effectively in uncertain, contact-rich environments. His research further extends to event-triggered mechanisms, fuzzy logic integration, and model-free sliding mode control, reflecting remarkable methodological breadth. With over 349 cumulative citations and consistent output from 2019 through 2024, An has established himself as a rising authority in intelligent robotic control, making his work essential reading for students and researchers in autonomous systems and human-robot interaction.
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