Stephen McIlvanna
Papers
12
Total Citations
68
H-Index
5
About
Stephen McIlvanna is an emerging robotics and control systems researcher whose work sits at the intersection of advanced control theory, human-robot interaction, and autonomous systems. His research primarily focuses on safety-critical control frameworks, sliding mode control, and physical human-robot collaboration (pHRC), with notable contributions to admittance control strategies that balance compliance and robustness in shared human-robot workspaces. McIlvanna's most influential work introduces fixed-time integral sliding mode controllers for admittance control, garnering 17 citations since 2023, alongside an adaptive admittance control framework for safety-critical pHRC scenarios with 15 citations — together establishing him as a rising voice in safe, responsive robot manipulation. His research extends across mobile robotics and autonomous underwater vehicles (AUVs), where he has developed model-free nonlinear Model Predictive Control (NMPC) schemes and Control Barrier Function (CBF) methodologies to ensure obstacle avoidance in dynamic, uncertain environments. His exploration of deep reinforcement learning for autonomous navigation and multi-agent AUV coordination further demonstrates impressive breadth. Accumulating over 60 citations across publications spanning just two years, McIlvanna's contributions are rapidly gaining recognition, making his research profile particularly valuable for students and practitioners working on next-generation safe autonomous and collaborative robotic systems.
Research Focus
Key Achievements
Top Papers
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