Daniele Brambilla

Papers

Total Citations

144

H-Index

About

Daniele Brambilla is a robotics and control systems researcher whose work has made meaningful contributions to the field of fault detection and diagnosis in robotic systems. His research focuses primarily on model-based fault detection techniques for robot manipulators, with a particular emphasis on leveraging second-order sliding mode observers to identify and isolate faults in actuators and sensors. Brambilla's most influential contribution, "Fault Detection for Robot Manipulators via Second-Order Sliding Modes" (2008), has garnered 116 citations, establishing it as a key reference in the fault detection literature. This work introduced a robust scheme capable of detecting and isolating single faults occurring at specific actuators or sensors, while also providing an estimation of the fault signal itself — a significant advancement for real-time diagnostic applications in robotics. Across his 2008 publication cluster, Brambilla demonstrated a consistent and systematic approach to the fault detection problem, exploring input signal estimators, output observers, and sliding mode frameworks to build reliable, computationally tractable detection schemes. His research addresses a critical challenge in autonomous and industrial robotics: ensuring operational safety and reliability when component failures occur. His work remains a valuable reference for researchers developing fault-tolerant control systems for robotic manipulators.