Andrea Giantomassi

About

Andrea Giantomassi is a leading researcher in the field of advanced robotics control, with a primary focus on discrete-time sliding mode control for robotic manipulators. His most impactful work, "Discrete time sliding mode control of robotic manipulators: Development and experimental validation," has garnered 103 citations, establishing a foundational approach for robust robotic control. Giantomassi’s key contribution lies in integrating neural networks—specifically radial basis function networks—with sliding mode control to handle system uncertainties. In his highly cited 2012 paper (61 citations), he introduced Minimal Resource Allocating Networks that dynamically grow and prune neurons to learn uncertainties online, significantly improving control stability. His 2014 work further refined quasi-sliding modes for robust arm control. Beyond robotics, Giantomassi has explored Ambient Assisted Living technologies for elderly independence (14 citations). His innovative combination of adaptive learning algorithms with variable structure control has made his methods experimentally validated benchmarks in the field, offering practical solutions for precise, uncertainty-tolerant robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Discrete time sliding mode control of robotic manipulators: Development and experimental validation

   103 citations · 2012
2. 2
   Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators

   61 citations · 2012
3. 3
   AAL Technologies for Independent Life of Elderly People

   14 citations · 2015
4. 4
   Robust Control of Robot Arms via Quasi Sliding Modes and Neural Networks

   11 citations · 2014
5. 5
   Discrete time sliding mode control of robotic manipulators: Development and experimental validation

   7 citations · 2011
6. 6
   Discrete time variable structure control of robotic manipulators based on fully tuned rbf neural networks

   4 citations · 2010

Key Collaborators

Contact & Links