Papers
23
Total Citations
400
H-Index
10
About
Francisco Rossomando is an Argentine researcher whose work sits at the intersection of robotics, control theory, and artificial intelligence, with a particular focus on adaptive neural network-based control systems for robotic platforms. Over more than a decade of sustained research, he has made significant contributions to the development of intelligent controllers capable of managing the complex, nonlinear dynamics inherent in both mobile robots and robotic manipulators such as SCARA arms. Rossomando's most influential contributions involve combining sliding mode control with neural network adaptation — a powerful hybrid approach that delivers robustness against model uncertainties and dynamic disturbances. His 2013 paper on sliding mode neuro-adaptive control for mobile robot trajectory tracking has accumulated 76 citations, establishing it as a cornerstone reference in the field. His broader body of work, spanning neural PID controllers, RBF neural compensators, and discrete-time adaptive architectures, reflects a consistent drive to eliminate the need for precise mathematical models in real-world robotic control. With cumulative citations exceeding 320 across his top works, Rossomando has built a meaningful legacy in intelligent robotics control. His more recent work on input-output linearization for skid-steering robots demonstrates continued relevance and an evolving research agenda that bridges theoretical rigor with practical implementation.
Research Focus
Key Achievements
Top Papers
- 1Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots76 citations · 2013
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- 3Autonomous mobile robots navigation using RBF neural compensator49 citations · 2010
- 4Adaptive Neural Sliding Mode Control in Discrete Time for a SCARA robot arm29 citations · 2016
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- 9Discrete-time sliding mode neuro-adaptive controller for SCARA robot arm16 citations · 2016
- 10