Gian Paolo Incremona
Papers
25
Total Citations
928
H-Index
12
About
Gian Paolo Incremona is a prominent robotics and control systems researcher whose work spans advanced sliding mode control, model predictive control, and deep reinforcement learning applied to robotic manipulators. His research has made substantial contributions to the field of robust motion control, most notably through the development of the Integral Suboptimal Second-Order Sliding Mode (ISSOSM) control algorithm, which elegantly eliminates the problematic reaching phase in traditional sliding mode approaches — a paper that has garnered over 210 citations. His hierarchical multiloop MPC framework combining inverse dynamics and integral sliding modes has similarly resonated widely, accumulating nearly 200 citations. Incremona has demonstrated a rare versatility by bridging classical control theory with modern machine learning: his pioneering work on deep reinforcement learning for real-time collision avoidance in robot manipulators has drawn over 100 citations, reflecting its timeliness and practical relevance to human-robot coexistence. He is also co-author of the graduate-level text *Advanced and Optimization Based Sliding Mode Control*, cementing his role as both innovator and educator. Across his portfolio, Incremona's research addresses safety, robustness, and autonomy in robotic systems — challenges that remain central to next-generation intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2MPC for Robot Manipulators With Integral Sliding Modes Generation194 citations · 2017
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- 4Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators92 citations · 2018
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- 10Sliding Mode Optimization in Robot Dynamics With LPV Controller Design13 citations · 2021