Nikolas Sacchi
University of Pavia, Ingegneria dei Sistemi (Italy), University of Genoa
Papers
8
Total Citations
45
H-Index
5
About
Nikolas Sacchi is a robotics and control systems researcher whose work sits at the intersection of deep reinforcement learning (DRL), sliding mode control, and intelligent automation for robotic systems. His research has made notable contributions to fault diagnosis, motion planning, and advanced control strategies for both industrial robot manipulators and biomedical applications such as robotic prosthetics. Sacchi's most cited work (13 citations) introduced a pioneering fault diagnosis framework for redundant manipulators, creatively combining DRL with sliding mode observers to detect and isolate sensor faults in real time. This reflects a recurring theme in his research: fusing classical model-based control theory with modern machine learning to overcome their individual limitations. His investigations into neural networks within integral sliding mode control, Lyapunov-based weight adaptation laws, and Linear Parameter Varying representations demonstrate both theoretical depth and practical rigor. Beyond industrial robotics, Sacchi has explored DRL-driven gait symmetry restoration for trans-femoral amputees, highlighting a genuine commitment to socially impactful engineering. His contributions to collision avoidance strategies, including scenario-based Deep Q-Networks and real-time configuration space methods, further demonstrate his versatility. With work spanning underwater ROVs to prosthetic limbs, Sacchi is establishing himself as a multidisciplinary voice in intelligent robotic control.
Research Focus
Key Achievements
Top Papers
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- 2On neural networks application in integral sliding mode control8 citations · 2024
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