The University of Texas at Arlington

🇺🇸 US

Papers

422

Total Citations

14,730

H-Index

47

Researchers

379

About

The University of Texas at Arlington (UTA) has established itself as a powerhouse in robotics, control systems, and artificial intelligence research, with decades of foundational contributions that continue to shape modern engineering. The institution's research portfolio spans an impressive range of specializations, including advanced control theory, neural network-based robotics, human-robot interaction, autonomous systems, and medical robotics — reflecting a deep commitment to both theoretical rigor and real-world applicability. UTA's most defining contributions lie in intelligent control systems. The landmark work on Sliding Mode Control in Electro-Mechanical Systems has amassed over 3,200 citations, cementing the university's role in shaping robust control methodology worldwide. Equally influential is its pioneering research on neural network controllers for robot manipulators — work published in the mid-1990s that demonstrated model-free control with guaranteed tracking performance, earning over 1,100 citations and helping define the trajectory of learning-based robotics. Complementing these efforts, UTA researchers have made significant strides in reinforcement learning and approximate dynamic programming, providing foundational algorithms now widely applied in feedback control. Beyond classical robotics, UTA has demonstrated remarkable breadth. Its contributions include quadrotor control using Lagrange dynamics, nonholonomic mobile robot navigation, satellite proximity operations, swarm formation control, and medical robotics — including novel magnetic anchoring systems for minimally invasive surgery. A comprehensive survey on robots in healthcare has attracted over 370 citations, reflecting the institution's growing influence at the intersection of AI and medicine. UTA's research centers, including efforts within its Automation & Robotics Research Institute (ARRI), provide prospective students and collaborators with access to world-class facilities and a collaborative culture committed to solving complex, high-impact challenges across autonomous systems, human-robot collaboration, and intelligent control.

Research Focus

Key Achievements

47
H-Index
422
Papers
14,730
Total Citations
379
Faculty & Researchers
🏆 Most Cited Paper
Sliding Mode Control in Electro-Mechanical Systems
3,211 citations · 2010
📊 Avg Citations/Paper: 35
📈 Most Prolific Year: 2016 (27)
🔬 Research Focus: Computer science, Artificial intelligence, Engineering, Control (management), Control theory (sociology), Robot

Top Papers

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Faculty & Researchers

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