Antonella Ferrara
University of Pavia, University of Genoa, University of Ferrara, Politecnico di Milano
Papers
64
Total Citations
2,175
H-Index
21
About
Antonella Ferrara is a prominent control systems and robotics researcher whose work spans robust control theory, sliding mode control, fault diagnosis, and intelligent robot motion planning. She is perhaps best known for her foundational contributions to sliding mode control methodologies, particularly her development of second-order and integral suboptimal sliding mode algorithms tailored for robot manipulators — work that has garnered over 200 citations and established her as a leading authority in the field. Her 2017 paper combining Model Predictive Control with integral sliding modes (194 citations) further demonstrates her ability to bridge classical and modern control paradigms. Ferrara has also made significant contributions to fault detection and diagnosis, employing higher-order sliding mode observers to identify actuator and sensor failures in robotic systems. Her early involvement in the AMADEUS underwater dexterous robot hand project (1997, 131 citations) reflects a long-standing commitment to real-world robotics applications. More recently, she has embraced deep reinforcement learning for collision avoidance and autonomous path planning, with papers from 2018 and 2020 attracting growing attention. Her 2019 book on optimization-based sliding mode control synthesizes decades of research, making her an essential figure for students and practitioners in advanced robotics and control engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2MPC for Robot Manipulators With Integral Sliding Modes Generation194 citations · 2017
- 3Manipulator Fault Diagnosis via Higher Order Sliding-Mode Observers160 citations · 2012
- 4AMADEUS: advanced manipulation for deep underwater sampling131 citations · 1997
- 5Fault Detection for Robot Manipulators via Second-Order Sliding Modes116 citations · 2008
- 6
- 7
- 8MIMO Closed Loop Identification of an Industrial Robot95 citations · 2010
- 9Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators92 citations · 2018
- 10