Papers
11
Total Citations
114
H-Index
5
About
Mathieu Brunot is a leading researcher in the field of system identification, with a primary focus on the dynamic modeling of industrial robots. His work addresses the fundamental challenge of accurately identifying robot parameters—such as inertia and friction—from noisy, closed-loop measurement data. Brunot’s major contributions center on developing advanced instrumental variable (IV) and prediction error methods that overcome the limitations of standard least-squares techniques, which require careful data preprocessing and can yield biased estimates. His most cited work, “An improved instrumental variable method for industrial robot model identification” (2018, 46 citations), presents a robust framework that leverages off-line velocity and acceleration estimation from joint position data alone. He has also pioneered recursive IV approaches and state-space estimation methods using Kalman filtering, as detailed in his 2017 paper on industrial robot arm identification (8 citations). Brunot’s research is notable for its pragmatic, systematic statistical analysis, ensuring that his methods are both theoretically sound and practically deployable in real-world manufacturing settings. With a cumulative impact spanning over 100 citations, his work has become essential reading for engineers and researchers seeking reliable, automated identification of rigid industrial robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Output Error Methods for Robot Identification24 citations · 2019
- 3A New Recursive Instrumental Variables Approach for Robot Identification8 citations · 2018
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- 7A Separable Prediction Error Method for Robot Identification4 citations · 2016
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- 9State Space Estimation Method for Robot Identification4 citations · 2016
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