Fabio Bonassi
Papers
1
Total Citations
9
H-Index
1
About
Fabio Bonassi’s research lies at the intersection of nonlinear system identification and model-based control, with a particular focus on neural architectures for dynamic systems. His most cited work, “Learning Control Affine Neural NARX Models for Internal Model Control Design” (2024, 9 citations), introduces the Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) framework—a novel approach that embeds the known control-affine structure of a system directly into a neural network model. This innovation bridges the gap between data-driven learning and classical control theory, enabling more accurate and reliable model-based control designs. By aligning the neural architecture with the physical structure of the system, Bonassi’s work enhances both interpretability and performance in nonlinear control tasks. His contributions are particularly impactful for students and researchers working on system identification, internal model control, and the integration of machine learning with control systems. With a growing citation record, Bonassi is establishing himself as a thoughtful voice in the push toward structure-aware neural control methods.
Research Focus
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Top Papers
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